Evaluating User Responses Based on Bootstrapped Knowledge Acquisition from a Limited Knowledge Domain

ABSTRACT

Mechanisms for training a human user to perform an operation and provided. The mechanisms generate a domain specific knowledge base comprising a set of entities and corresponding domain specific attributes and expand the domain specific knowledge base to include values for the domain specific attributes through an automated bootstrap learning process that performs natural language processing and analysis of natural language content using a set of pre-condition annotated action terms, thereby generating an expanded domain specific knowledge base. The mechanisms evaluate an input from another device identifying an action associated with an entity in the set of entities, based on a retrieved domain specific attribute value and the retrieved pre-condition annotation from the expanded domain specific knowledge base. The mechanisms output a notification to a user computing device indicating whether the input is correct or incorrect to thereby train a user associated with the user computing device.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for evaluatinguser responses based on bootstrapped knowledge acquisition from naturallanguage content.

When reading through natural language content, such as recipes from acooking domain, real-world common sense knowledge provides constraintson an ingredient and the actions to be performed on the ingredient. Forexample, it is common sense that cottage cheese cannot be grated.However, in the absence of such common sense, it is difficult to learnsuch knowledge from simply reading a recipe. Further, when asked toproduce a recipe for a particular dish, not being aware of the variousactions that can be performed on an ingredient can produce nonsensicalresults. For example, when requested to produce a recipe for a cheesefondue from a round wheel of cheese, one may cut, slice, grate, or meltthe cheese, but one may not pour the cheese until after the cheese hasfirst been sliced and then melted.

Recently, International Business Machines (IBM) Corporation of Armonk,N.Y., has released an intelligent cooking recipe application referred toas IBM Chef Watson™. IBM Chef Watson™ searches for patterns in existingrecipes and combines them with an extensive database of scientific(e.g., molecular underpinnings of flavor compounds) and cooking relatedinformation (e.g., what ingredients go into different dishes) withregard to food pairings to generate ideas for unexpected combinations ofingredients. In processing the database, IBM Chef Watson™ learns howspecific cuisines favor certain ingredients and what ingredientstraditionally go together, such as tomatoes and basil. The applicationallows a user to identify ingredients that the user wishes to include inthe recipe, ingredients that the user wishes to exclude, as well asspecify the meal time (breakfast, lunch, dinner), course (appetizer,main, dessert), and the like.

The IBM Chef Watson™ has inspired the creation of an IBM Chef Watson™food truck, a cookbook entitled Cognitive Cooking with Chef Watson,Sourcebooks, Apr. 14, 2015, and various recipes including a barbecuesauce referred to as Bengali Butternut BBQ Sauce.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described herein in the DetailedDescription. This Summary is not intended to identify key factors oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

In one illustrative embodiment, a method is provided, in a dataprocessing system includes a processor and a memory accessible by theprocessor, for training a human user to perform an operation. The methodcomprises generating, by the data processing system, a domain specificknowledge base comprising a set of entities and corresponding domainspecific attributes. The method further comprises expanding, by the dataprocessing system, the domain specific knowledge base to include valuesfor the domain specific attributes through an automated bootstraplearning process that performs natural language processing and analysisof natural language content using a set of pre-condition annotatedaction terms, thereby generating an expanded domain specific knowledgebase. Moreover, the method comprises obtaining, by the data processingsystem, an input from another device identifying an action associatedwith an entity in the set of entities, and retrieving, by the dataprocessing system, from the expanded domain specific knowledge base, adomain specific attribute value for the entity identified in the inputand a pre-condition annotation associated with the action identified inthe input. In addition, the method comprises evaluating, by the dataprocessing system, a correctness or incorrectness of the input based onthe retrieved domain specific attribute value and the retrievedpre-condition annotation. Furthermore, the method comprises outputting,by the data processing system, a notification to a user computing deviceindicating whether the input is correct or incorrect to thereby train auser associated with the user computing device.

In other illustrative embodiments, a computer program product comprisinga computer useable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer (QA) system in a computer network;

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments are implemented;

FIG. 3 illustrates an example of a cognitive system processing pipelinewhich, in the depicted example, is a question and answer (QA) systempipeline used to process an input question in accordance with oneillustrative embodiment;

FIG. 4 illustrates an example recipe upon which the mechanisms of theillustrative embodiments may operate;

FIG. 5 is a flowchart outlining an example operation for performing anautomated bootstrap learning process in accordance with one illustrativeembodiment;

FIG. 6 is a flowchart outlining a cognitive operation that may beperformed by a cognitive system in accordance with one illustrativeembodiment;

FIG. 7 is an example block diagram illustrating one illustrativeembodiment of a testing/training system employing the knowledgeacquisition system of the illustrative embodiments;

FIG. 8A is an example diagram of a training/testing system prompt outputthat may be provided to a user via a user client system in accordancewith one illustrative embodiment;

FIG. 8B is an example diagram of an example response of atraining/testing system to a user input in accordance with oneillustrative embodiment;

FIG. 9 is a flowchart outlining an example operation for providing atraining/testing functionality in accordance with one illustrativeembodiment;

FIG. 10 is an example block diagram illustrating one illustrativeembodiment of an automated instruction execution system employing theknowledge acquisition system of the illustrative embodiments; and

FIG. 11 is a flowchart outlining an example operation for performingautomated correction/insertion of instructions for an automated systemin accordance with one illustrative embodiment.

DETAILED DESCRIPTION

The illustrative embodiments provide mechanisms for bootstrapping asmall amount of manually acquired domain knowledge to automaticallybuild a much larger set of domain knowledge, where the knowledgeacquired includes the usage constraints on entities in the domain,expressed as feature compatibilities between an entity (such as anobject) and a verb (which is an action to be performed on, to, or withthe object). The mechanisms of the illustrative embodiments may beapplied to many different types of domains in which learning of therelationships between actions and entities is desirable, and especiallyin domains where an ordered series of tasks or operations are performedon an object, with each task or operation potentially changing theobject's state. For example, the illustrative embodiments may beimplemented with regard to the medical and scientific laboratory domain,art domain, financial domain, criminal investigation domain, and aplethora of other domains.

One example of these many domains for which this mechanism may beapplied, and which will be used as an example throughout thisdescription for non-limiting illustration purposes, is the cookingdomain in which real-world common sense knowledge provides strongconstraints on entity/action compatibility, such as knowing that“cottage cheese” cannot be “grated.” The illustrative embodiments areadvantageous from other solutions to acquire knowledge, such as commonsense knowledge that human beings take for granted, because while thereis initially a minimal manual activity related to seeding a collectionof domain knowledge, thereafter the embodiments automatically augmentthe collection of domain knowledge from ingesting a corpus of documentsfrom the relevant domain (it should be appreciated that references to“documents” in the present description means electronic documentsprovided as data structures that are able to be processed by a computingdevice). As time progresses, the collection of domain knowledge growsbuilding common sense knowledge about entity/verb (or action)compatibility.

The illustrative embodiments are thus, directed to solutions for solvingthe problems associated with computing devices, and in particularcognitive systems, with regard to the amount of knowledge that thecomputing devices or cognitive systems have to perform their operations.That is, it should be appreciated that while it is relatively straightforward for human beings to recognize constraints on entity/verbcompatibility because human beings already have a collection ofknowledge about entities, actions, and language usage that informs themof the attributes of entities which the human being can correlate withthe constraints of the verb, this is not readily understandable bycomputing systems which do not have such a collection of knowledge, orhave relatively limited knowledge based on whatever the human beingsusing those computer systems have provided them.

For example, while a human being may readily understand that one cannot“pour” bread, a computer may not readily have this knowledge when it isanalyzing the terms “pour” and “bread” in natural language, unless ithas additional knowledge about the attributes and constraints associatedwith these terms given to it by a human being, e.g., that “pouring” isan action performed on objects that are liquid or sometimesparticulates, while “bread” is a solid. This is because computingdevices do not “think” in the same way that human beings do and mustspecifically be given information upon which to base its operations,i.e. they do not have any perception of the real world. Generating setsof information for use by computing devices performing cognitiveoperations using natural language processing mechanisms in particular isproblematic both from the view point of the amount of time and resourcesrequired to generate such information such that it may be presented to acomputing device, but also in terms of the human limitations and errorsthat lead to gaps in such information.

Moreover, it is difficult to extract knowledge from natural languagecontent itself. This difficulty arises from several facts. First, thecomputing system attempting to gather the knowledge, such as foridentifying incompatibilities between objects and actions (entity/verbs)must be able to differentiate combinations where the knowledge simplydid not appear in the training data used to train the computing systemversus actual incompatibilities where the actions just do not make sensewith regard to the entities, e.g., the computer just does not knowwhether the entity/verb pair is unusual or the entity/verb pair is infact incorrect.

Second, many times, such as in the case where a sequenced set of actionsor tasks are being performed on the same or a variety of differentobjects, the knowledge that needs to be known may be with regard toentities/actions that are modified over the course of the naturallanguage content describing the sequenced set of actions or tasks, andthe computing system would not be aware of this modification from thenatural language content itself. For example, in the cooking domain,butter may initially be in a solid state but is sometimes melted andthen poured. Thus, using known mechanisms that merely look atco-occurrence of terms, e.g., a document that comprises natural languagecontent specifying that pour has a co-occurrence with butter, theknowledge that would be gained from the text would be that butter may bemelted and butter may be poured, without actually understanding thatdifferent states of the entity are required for these different actionsto be performed, i.e. the computer system would only know that buttercan be poured and would not know that the butter must first be put intoa liquid state before being poured, such as by way of melting a solidstate of the butter to make a liquid state of the butter. That is, thecomputer does not know that different actions require or are constrainedfor use with entities having specific states.

Third, many times the knowledge that needs to be learned may be part ofgeneral common sense knowledge and thus, is not specifically statedexplicitly in the natural language content itself. For example, anatural language electronic document that describes an onion, such asthe Wikipedia™ online encyclopedia entry for “onion”, may not state thatthe onion is a solid object since this is general common sense to humanbeings, even though this knowledge is not “common sense” for thecomputing device. Again, the computing device does not have the realworld experience and knowledge that is inherently gathered throughexistence as does a human mind and thus, a computing device must beexplicitly given the information upon which it can perform operations orelse that information simply does not exist in the view of the computingdevice.

In addition to the above, with cognitive systems that are trained usingtraining set data, the natural language content that is used fortraining is rife with missing entity arguments and therefore, providesvery sparse coverage for the knowledge that needs to be acquired. Thatis, it is desirable to have complete coverage of the attributes of anentity, in an ontology of concepts, for which the knowledge is beingacquired, e.g., in the cooking domain it is desirable to know the stateof matter attributes for all ingredients in an ingredient database.However, most attribute assignment methods, such as manually populatinga knowledge graph with entities and their attributes, allow for sparsecoverage of the entity attributes, e.g., not all of the entities in aknowledge graph or ontology will have the same attributes populated withvalues or have the same set of attributes themselves. That is, oneentity may have an attribute of “color” while another may not have thatattribute and instead may have an attribute of “texture”. This presentsproblems for various cognitive systems which require concepts in anontology to be labeled with certain attributes, common across allentities in the knowledge graph or ontology, in order for thosecognitive systems to be able to perform their cognitive operations. Thatis, if the knowledge source does not have the attributes required by thecognitive system, then the cognitive system cannot operate properly dueto a lack of knowledge.

For example, in the IBM Chef Watson™ cognitive system, it is importantto know the state of matter of ingredients, e.g., solid, liquid,particulate, etc., whether the ingredient is chewable, and a number ofother attributes of ingredients that lead to cognitive decisions beingmade by the IBM Chef Watson™ cognitive system. Acquiring thisinformation manually is a daunting task as the number of ingredientsincreases (currently there are over 3600 ingredient entries). Acquiringmultiple features for all of these ingredients manually may take severalmonths, or even years, of human effort. Moreover, with such a largenumber of attributes having to be populated by human beings, errors areinevitable and can cause significant performance problems for thecognitive system.

As noted above, in a first aspect of the illustrative embodiments,mechanisms are provided for bootstrapping a small amount of manuallyacquired domain knowledge to automatically build a much larger set ofdomain knowledge, where the knowledge acquired may be based on the usageconstraints of actions being performed on entities in the domain,expressed as feature correlations or compatibility between an entity,e.g., represented as a noun in natural language content, and an action,e.g., represented as a verb in natural language content. Theillustrative embodiments utilize this small set of domain knowledge,along with some knowledge and assumptions about the language of thenatural language content, to infer attributes of entities and therebyexpand the domain knowledge. It should be appreciated that the term“domain knowledge” as it is used herein refers to knowledge aboutattributes of entities where those attributes are important toperforming cognitive operations in a specific domain. For example,domain knowledge for a knife in the cooking domain may include anattribute that a knife “cuts” or “slices” ingredients. However, domainknowledge for a knife in a criminal investigation domain may includeattributes such as “weapon” or that the knife “stabs” a victim.

In some illustrative embodiments, a small set of actions (e.g., verbs)are manually tagged with features that indicate constraints on theattributes of entities with which that action may be utilized, e.g., theverb “pour” has a feature that it may be used with entities having astate of matter attribute of “liquid.” This relatively small set ofaction terms, or verbs, are then used to analyze natural languagecontent to infer the attributes of entities by observing the use of theaction terms in conjunction with the entities in the natural languagecontent, e.g., a particular ingredient is an argument of that actionterm (or verb). The inferred attributes of entities generated from theanalysis of the usage of action terms, or verbs, in the natural languagecontent in conjunction with an entity term for the entity, may be addedto a knowledge base entry for that entity to thereby populate a requiredattribute field for the entity upon which the cognitive system mayoperate. In this way, the knowledge base for the cognitive system isautomatically expanded through cognitive analysis of natural languagecontent using a small initial set of tagged action terms. Hence, arelatively small amount of human effort is expended through a focusedeffort to tag a small set of action terms which can then beautomatically utilized to generate a relatively larger entity attributeknowledge base.

It should be appreciated that, as touched on above, the attributes ofentities inferred through the mechanisms of the illustrativeembodiments, may not be explicitly provided in the natural languagecontent itself and may not have been manually provided to the cognitivesystem prior to encountering the entity in natural language content. Inaddition, information may be inferred by the mechanisms of theillustrative embodiments regarding changes in attributes of entities.That is, based on the order of the natural language content and theorder in which the action terms are present in the natural languagecontent in correlation with the entity, the mechanisms of theillustrative embodiments may determine a proper ordering of attributeinformation for the entity. For example, in the cooking domain, if arecipe indicates that butter is first “melted” and then “poured” and, inaccordance with the illustrative embodiments, the small set of actionterms indicate that the term “melt” or “melted” corresponds to entitieshaving a state of matter attribute of “solid”, then it can be determinedthat butter is a solid. Moreover, because the small set of action termsindicate that the term “pour” or “poured” corresponds to entities havinga state of matter attribute of “liquid,” it can further be inferred thatbutter is also a “liquid”. Based on the ordering of the natural languageof the recipe, the illustrative embodiments may determine that butter isinitially a solid and can be melted to generate a liquid which can thenbe poured.

It should be noted that this information is not explicitly stated in thenatural language content itself. All that the natural language contentindicates is that the butter is to be melted and the butter is to bepoured. The knowledge that butter is initially a solid is not present inthe natural language content. The knowledge that melting buttergenerates liquid butter is not present in the natural language content.The knowledge that melting butter to generate liquid butter then allowsone to pour the butter is not present in the natural language content.Thus, the mechanisms of the illustrative embodiments expand theknowledge of the ingredient “butter” through automated mechanism thatinfer attributes about butter which are not present in the naturallanguage content itself, by observing the usage of a small set of actionterms (or verbs) in the natural language content and inferringattributes of the entity based on the features or constraints associatedwith the action terms.

In one example implementation of the mechanisms of the illustrativeembodiments, initially it is determined what attributes of entities areto be learned in order to expand the knowledge base for the particulardomain, e.g., the knowledge base of ingredients in a cooking domain. Forexample, in the cooking domain, it may be desirable to learn the stateof matter attribute of each ingredient in a knowledge base or ontology.

In addition, for a collection of action terms (or verbs) in a domainspecific corpus, pre-condition features are defined for the action termsthat are specific to those attributes of the entities that are to belearned. For example, in the cooking domain, if the attribute of theingredients that is desired to be learned is a state of matterattribute, then the pre-condition features may specify the types ofstate of matter that the action term operates with, e.g., the action of“pouring” is used with entities having a state of matter of “liquid.” Itshould be appreciated that each action term may have a set of domainspecific synonyms associated with it including various forms of theaction terms, equivalent action terms, and the like, which may bespecified in a resource data structure, such as a synonym dictionary ormapping data structure, and the features of one action term may beassociated with each of its synonyms.

With this relatively small set of action terms and their associatedpre-condition features that are defined specifically with regard to theattributes of entities that are attempting to be learned, documents of adomain specific corpus, comprising natural language content, areanalyzed as part of an automated learning process. The automatedlearning process, for each document, arranges the recognized actionterms found in that document into a correct temporal ordering. Forexample, in the cooking domain, recipe instructions are expected to beprovided in a correct temporal ordering and thus, the order of theaction terms will match the ordering of the execution of the recipeinstructions. However, in other domains, such ordering may not beimplicit and more complex temporal analysis of the natural languagecontent may be performed, such as looking for temporal terms indicatingan ordering, e.g., “before,” “after”, “next,” “and then”, etc. Any knownor later developed mechanism for determining temporal ordering based onanalysis of natural language content may be used without departing fromthe spirit and scope of the present invention. One example mechanism maybe the mechanism for discovering temporal sequences described in Bramsenet al., “Finding Temporal Order in Discharge Summaries,” AMIA 2006Symposium Proceedings, pages 81-85.

With the action terms of the document having been ordered according totemporal characteristics, the first action term that operates on orreferences an entity of interest is identified, e.g., the first time inthe ordered action terms that a cooking action term (e.g., an actionterm of “chop”) operates on or references an ingredient entity (e.g., aningredient entity of “turnips”) is identified. It should be noted thatentities of interest may be specified, a priori, in a domain specificontology data structure or knowledge base data structure, for example.For the first occurrence of an action term operating on or referencingan entity of interest, the automated learning process assumes that theentity of interest satisfies the pre-condition features of the actionterm. That is, if the action term is “chop” and the pre-conditionfeature for “chop” is that the ingredient must have a state of matterattribute of “solid”, then it is assumed that the entity of interest,e.g., “turnips”, are solid and thus, satisfy the constraint of theaction term. Thus, the pre-condition feature of the action term is thenapplied to the entity as an attribute of the entity, i.e. the entity isnow recognized as having the attribute corresponding to thepre-condition feature.

This newly learned attribute for the entity may be generalized up adomain specific subsumption hierarchy, such as may be provided in ahierarchical ontology data structure for the domain, so that theattribute may be associated with other entities and concepts in thehierarchy. For example, the automated learning process may determinethat onions, potatoes, and turnips all have substantial evidence ofhaving a state of matter attribute of “solid.” From this, it can beinferred that their common ancestor, e.g., “root vegetable,” in asubsumption hierarchy of an ontology also has a state of matterattribute of “solid.” This propagated knowledge may then be used toreason about other less common root vegetables for which there may be noprior knowledge. For example, a “skirret” is a root vegetable and thus,from the automatically learned attributes of onions, potatoes, andturnips, and the generalization of the learned attributes up a domainspecific subsumption hierarchy, the learned knowledge can be applied toa “skirret” to infer that it also has a state of matter attribute of a“solid” since it is a root vegetable and root vegetables have the stateof matter attribute of “solid.”

It should be appreciated that, for some action terms, the pre-conditionfeatures may be specified in a negative manner, e.g., that the action isperformed on entities that are “non-solid”. In such a case, thepre-condition feature still provides evidence of an attribute of theentity, however, this attribute may not be specific. That is, forexample, if the pre-condition feature states that the action isperformed on “non-solid” entities, in the context of a cooking domain,the inferred attribute for the entity is “non-solid” but it still cannotbe determined, without further evidence, whether that entity, e.g., aningredient, has a state of matter attribute of “liquid” or“particulate”. All that is known at that time is that the ingredient isnot a solid. This is still more knowledge that was previously availablefor the entity and, with other evidence found through analyzing otherdocuments, may eventually lead to an inference that the particularentity has a state of matter attribute of either “liquid” or“particulate.”

It should also be appreciated that, in some illustrative embodiments,this process may be repeated for multiple documents of a corpus and/orportions of documents of a corpus, and each of the action term/entitypairings may be encountered in more than one of these documents and/orportions of documents. As such, evidence may be gathered from thevarious documents for different values of the attributes of an entity,e.g., one document may indicate butter is a solid while another documentmay indicate that butter is a liquid looking at the first occurrence ofan action term. The evidence for the various values of the attributes ofentities subject to the automated learning process obtained fromanalysis of the various documents of the corpus may be accumulatedacross these documents to generate evidential scoring data indicating aconfidence that a particular value for the attribute is correct. Forexample, a confidence score for each attribute value of an entity foundacross the multiple documents or portions of documents may be calculatedfrom an accumulation of the number of instances for each attribute valueencountered across the multiple documents, e.g., 86 instances of butterbeing a solid and 14 instances of butter being a liquid across 100documents. This evidential scoring data, i.e. confidence scores, may becompared to one or more thresholds to determine whether the amount ofevidence is sufficient to associate the value with the attribute of theentity. It should be appreciated that some attribute may have multiplevalues that are valid, e.g., while a state of matter attribute ismutually exclusive other attributes, such as color, may be discovered tohave variations for one entity, such as carrots. Alternatively, multipleinstances of an entity may be generated with each instance having adifferent value of the attribute, e.g., a first instance of butter thatis solid and a second instance of butter that is liquid. For thoseattribute values that meet or exceed the requirement specified by thethreshold(s), those attribute values are maintained in association withthe entity data structure in the knowledge base or ontology datastructure.

The result of this automated learning process is an expanded knowledgebase that is expanded with regard to the attributes of the entities forthe specific domain or in some cases additional entities andrelationships in the knowledge base, e.g., a new entity of “liquidbutter” may be generated and linked to a butter entity or to a commonroot entity. This expanded knowledge base may be represented as anontology database in which attributes of entities that may havepreviously been unpopulated are now populated by the knowledge gatheredthrough the automated process described herein. Moreover, this expandedknowledge base may be represented in an ontology database by anexpansion of the ontology to include additional entities that may nothave previously been in the ontology and which include attributeslearned through the automated learning process described herein.

The expanded knowledge base may then be utilized to perform variouscognitive operations including, but not limited to, performing questionanswering or responding to requests for information, correction ofnatural language documents or text, selecting the correct action wordwhen translating to a different human language, expanding upon thecontent of a natural language document, training/testing of human users,automatic generation of instructions or commands for controlling theoperation of an automated system or device, performing monitoring ofhuman actions or interactions with entities and providing constructivefeedback or instruction, and/or the like. More details regardingspecific embodiments in which various ones of these cognitive operationswill be described in greater detail hereafter.

In further illustrative embodiments, in addition to learning theattribute of the entity from the first occurrence of an action termoperating on or referencing the entity as described above, the actionterm may further have associated features that indicate the result ofthe action being performed on an entity with regard to an attribute ofthe entity that is to be learned, with these features being referred toherein as “post-condition” features. For example, melting a solid“post-condition” a liquid. Thus, not only can it be determined what theinitial attribute of the entity is when the action corresponding to theaction term is applied, but the result of that action on the entity mayalso be determined and associated with the entity, e.g., butter is asolid and a liquid and that melting solid butter results in liquidbutter. In some cases, this may spawn the generation of new entityinstances, e.g., a first instance for solid butter and a second instancefor liquid butter, thereby further expanding the ontology of entitiesand concepts for the specific domain.

Applying this knowledge, associated with the action term, to thetemporally ordered action terms, the automated learning process maydetermine the veracity of subsequent action term/entity pairings for theentity in a document or portion of a document and, for those actionterm/entity pairings deemed to be corrected, may associate attributes tothe entity based on the constrains of the subsequent action term orotherwise modify an evidential scoring for an attribute value of theentity. For example, in the cooking domain, assume that a recipeincludes the steps of melting the butter and then pouring the butter. Asnoted above, the first instance of an action term (melting) operating onthe entity (butter) may be analyzed to associate a pre-condition feature(solid entity) of the action term (melting) to an attribute (state ofmatter) of the entity (butter). In addition, a post-condition featuremay also be associated with the action term that indicates what theresult of the action is with regard to the particular attribute ofinterest, e.g., melting butter results in a liquid (post-conditionfeature).

This information may then be used to test the veracity of the nextoperation performed on the same entity. In this example, the second stepis to pour (action term) the butter (entity). The action term “pour” hasa pre-condition feature that it operates on liquid entities. A check ismade as to whether the temporally previous action term, in thetemporally ordered action terms for this entity, has a post-conditionfeature of “liquid” matching the constraint of the second action term,thereby indicating that the previous action generated a liquid form ofthe entity. If the post-condition feature matches the pre-conditionfeature of the next action term in the temporally ordered action termlist for this entity, then the second action term is correct and furtherknowledge about the entity may be obtained by performing a similaranalysis as before. That is, a similar analysis of correlating thepre-condition feature of the second action term with an attribute of theentity may be performed. Alternatively, the operation may increase theamount of evidential support for the value of the attribute of theentity obtained from the “post-condition” feature of the previous actionterm, e.g., the post-condition feature of the action term melt indicatedthat butter is a liquid and the subsequent action term, having amatching state of matter attribute on its pre-condition feature,provides further evidence that butter is a liquid and hence, theevidential scoring data is increased. If the results feature does notmatch the pre-condition feature of the next action term, then theprocessing for this entity with regard to this document may bediscontinued as each subsequent action item is likely incorrect due tothe temporal nature of the actions. This process may continue throughoutthe document and thus, a plurality of action term instances within asingle document may contribute knowledge with regard to the attributesof an entity, rather than being limited to the first such occurrence ofan action term.

As discussed hereafter with regard to one or more illustrativeembodiments, this process of traversing the action terms in thetemporally ordered listing of action terms for an entity may be utilizedto identify errors in a document, errors in responses from human users,or the like. The identified errors may be correlated with corrections orinstructions to correct the content of the documents or instruct a humanbeing or automated system with regard to the correct temporal orderingof actions to be performed on an entity with regard to the particularobjective that is to be achieved, e.g., preparing a recipe,manufacturing an object, performing a laboratory test, or the like.These corrections or instructions may be based on the knowledge presentin the expanded ontology or knowledge base generated by way of themechanisms of the illustrative embodiments, e.g., if a user beingmonitored or trained indicates that they are going to pour the butter,or a recipe being analyzed indicates that the next step is to pour thebutter, yet there is no indication that the butter has been previouslymelted, then an error may be identified and a corresponding correctionor instruction indicating that the butter should be melted or otherwiseconverted into a liquid state of matter will be output. Similarly, if ahuman being, document, or automated system indicates to “grate” the“cottage cheese,” it can be determined from the attributes of cottagecheese and the pre-condition features that there is a mismatch betweenthe constraint requirements for the action term “grate” and theattributes of “cottage cheese” and the source of the input, e.g. humanbeing, document, or automated system, may be informed of this error.

Thus, in a first aspect of the illustrative embodiments, the mechanismsof the illustrative embodiments provide for automated learning based ona relatively small set of annotated terms, e.g., action terms or verbs,in which the terms are annotated with pre-condition feature informationspecifying a requirement of an attribute of an entity upon which theaction corresponding to the action term may be performed. Moreover, theterms may optionally be annotated with post-condition featureinformation indicating what results from the action corresponding to theaction term being performed on an entity meeting the constraint of thepre-condition feature. This relatively small set of annotated terms arethen used as a bootstrapping mechanism for gathering knowledge about theattributes of entities in an ontology or knowledge base throughinference obtained from the usage of the annotated terms in naturallanguage content in documents or portions of documents of a domainspecific corpus. The expanded knowledge base or ontology may then beused to perform cognitive operations with a greater amount of knowledgebeing made available for use by the cognitive operations.

This benefit is obtained through an automated process which greatlyreduces human effort when representing knowledge about entities in anontology. For example, in a cooking domain, there may be 4000ingredients, represented by entity data structures in a knowledge basedor an ontology data structure, which may be utilized by a cognitivesystem, such as IBM Chef Watson™, for example. However, there may be arelatively small number of actions, and their corresponding actionterms, that can be performed on these ingredients, e.g., 200 actionterms and their synonyms corresponding to these actions, e.g., 300synonyms, that can be performed on ingredients, e.g., “chop,” “cut,”“grate”, “drizzle,” “pour”, etc. It takes considerably less time toannotate the 200 action terms with regard to pre-condition features andoptional post-condition features, than it does to annotate the variousattributes of each of the 4000 ingredients, where there may be many moreattributes of an ingredient than the number of features annotated forthe action terms. A greater time savings is also achieved since of the500 action terms, a smaller subset, e.g., the 200 original action terms,may only need to be annotated while other terms that are synonyms, e.g.,the 300 synonym terms, may need to only be mapped to the annotated termsthrough synonym data structures of dictionaries. With the mechanisms ofthe illustrative embodiments, the relatively small set of annotatedaction terms are used to learn the correct values for the attributes ofthe much larger set of ingredients based on the inference mechanisms ofthe automated learning process previously described above.

In addition to this first aspect, the illustrative embodiments, in asecond aspect, provide for an automated training/testing system thatprovides user feedback and/or question and answer generation fortraining/testing human beings regarding how to perform operations thatcomprise a series of ordered tasks, where the attributes of an entityinfluence which tasks in the series can be performed on the entity. Forexample, the training/testing system of these illustrative embodimentsmay be used, again using the cooking domain as an example, to train achef to prepare a recipe where the chef is questioned as to the steps ofthe recipe and the correctness of those answers is determined based onthe current attributes of the ingredients. For example, thetraining/testing system may monitor the actions of a human being, theresponses to questions presented to a human being, or may simply outputan ordered set of instructions to a human being. The monitoring of thehuman being may be performed through multi-media monitoring mechanisms,such as video monitoring equipment and image analysis that correlatescaptured images with actions being performed by a human being on arecognized entity identified through image recognition (e.g. an image ofa human cutting an apple, or pouring a liquid, etc.), audio responsemonitoring which may convert audio input to textual form which may thenbe processed using the mechanisms of the illustrative embodiments, orthe like. Responses to questions may be performed through aprompt/response type of interface where the person is prompted by aquestion and a corresponding answer is returned or selected from apredefined listing of potential answers. This may be done using anyvisual or audible mechanism including traditional computing systeminput/output devices, voice recognition mechanisms, and/or the like.

In one example, assume that the training/testing system has acquired theknowledge, using the mechanisms of the illustrative embodiments, thatbutter may be a solid, butter may be a liquid, and solid butter may bemelted to generate liquid butter. If the training/testing system istesting a person regarding the correct steps for performing a recipe inwhich butter is utilized, if the chef states that the next step of therecipe is to pour the butter, but the chef has not melted the butteryet, then the system would return a result that the answer is incorrectand may present the reason for the answer being incorrect, as well as acorrect response that the person should have provided. All of this maybe determined from application of the knowledge obtained through theautomated learning process using the bootstrapped annotated set ofaction terms as discussed above, for example. Moreover, a similaranalysis as presented above for expanding the knowledge baseautomatically, may be used to evaluate the current state of the entitywithin the scope of the objective attempting to be performed anddetermining the correctness of an action on the entity based on itscurrent state, previous state, the post-condition feature of theprevious action performed on the entity, and the pre-condition featureof the action that the human being is attempting to perform on theentity or the action indicated in the human being's response.

As noted above, such illustrative embodiments may be applied to anydomain where there are ordered tasks that must be followed to completean operation, and in which the temporal state of an entity influences orconstrains the types of actions that may be performed on the entity.Thus, while the illustrative embodiments are described in the context ofa cooking domain and the IBM Chef Watson™ cognitive system being used asa basis for performing cognitive operations, the illustrativeembodiments are not limited to such. Rather, the mechanisms of theillustrative embodiments may be applied to other domains such asmanufacturing, medical analysis (such as in the case of laboratory testsor experiments), scientific experimentation, criminal forensic scienceinvestigations, legal domains where the entities may be legal entities,or the like. Those of ordinary skill in the art will recognize theplethora of other applications of the mechanisms of the illustrativeembodiments to various domains in view of the description set forthherein and any such implementation of the illustrative embodiments isintended to be within the spirit and scope of the present invention.

In a third aspect, the illustrative embodiments provide mechanisms forperforming automatic insertion or correction of an ordered set of tasksfor completing an operation or achieving an objective in a particulardomain. The insertion/correction may be performed, in some illustrativeembodiments, to augment or otherwise complete an already existing set oftasks or instructions for performing tasks. For example, again using thecooking domain as an example, a recipe may be provided that has missingor incorrect instructions. The mechanisms of the illustrativeembodiments may be implemented to verify the entity/action paircorrectness and thereby determine if there are missing instructions inthe recipe and what the nature of those missing instructions may be withregard to correction entity/action pairs. As a simple example, considera recipe that calls for the pouring of butter into a mixture. The recipemay not previously have had an instruction to melt the butter prior tothe pouring instruction.

Thus, the mechanisms of the illustrative embodiments may parse therecipe and generate a temporally ordered action list for each entity inthe recipe. The temporal state of the corresponding entity may betracked by traversing the action terms in the temporally ordered actionlist, e.g., the butter is first melted and thus, the state of the butterwent from solid to liquid, the butter was then poured into a mixture andthus, the state of the butter is a mixture state (which may indicatethat actions to the butter by itself can no longer be performed andthus, subsequent actions that do not reference the mixture and insteadreference the butter may be erroneous). The temporally ordered actionlist may be processed to determine if there are any mismatches betweenpre-condition features of action terms and the then temporal state ofthe entity.

If there are mismatches, then it may be determined that there is amissing instruction or set of instructions in the recipe. For example,assume that a first action term indicates that the butter has beendivided meaning that the butter was initially in a solid state and isrendered into a divided solid state. The next action term in thetemporally ordered action term listing may indicate that the butter isto be poured into a mixture. The pre-condition feature of the actionterm “pour” requires a liquid entity, e.g., a liquid form of aningredient, however by tracking the temporal state of the entity, it isdetermined that the current state of the butter entity is a dividedsolid. As a result, a mismatch is identified between the pre-conditionfeature of the action term and the current state of the entity. Theerror may be noted and a corresponding notification generated. In someillustrative embodiments, the detection of this mismatch or errorindicates missing instructions and an instruction knowledge base may besearched for instructions that result in the required attribute for theentity required by the pre-condition feature of the action term, e.g.,an instruction that results in liquid butter for use with a subsequentaction term that requires liquid butter as part of its pre-conditionfeature.

Thereafter, the set of instructions may be automatically corrected orupdated to include any missing instructions required to provide theentity with the required attribute value. Of course, additionalcognitive operations may be performed to ensure compatibility of thediscovered instructions with the other instructions present in theexisting listing of instructions and with common sense knowledge. Forexample, using only the instructions themselves, the instruction “gratethe cottage cheese” may be corrected by inserting an additional priorinstruction to “freeze the cottage cheese” (this would make the cottagecheese solid) and then “grate the cottage cheese”. However includingcommon sense knowledge may indicate that“freezing cottage cheese” mightnot be a good idea as cottage cheese is generally not frozen.

, e.g., in a recipe, set of manufacturing instructions, or the like. Forexample, the IBM Chef Watson™ cognitive system comprises cognitive logicfor identifying certain compatibilities of ingredients based on theirtexture, color, taste, and the like, which may be taken intoconsideration when evaluating the inclusion of new instructions toaddress the instruction gaps or mismatches found via the mechanisms ofthe illustrative embodiments.

Similarly, at each stage of checking the pre-condition features of anaction and the current state of the entity, it may also be determinedwhether an action term requires a certain attribute of the entity andwhether or not the entity can or cannot have that value of theattribute, e.g., none of the values for the attribute exist inassociation with the entity, then an error may be identified. Forexample, if it is determined that an action term has a pre-conditionfeature that the entity must be in liquid form, e.g., the action term“pour”, but none of the state of matter attribute values for the entityallow for a liquid state of matter, then an error is determined forwhich no missing instructions can be provided. In such a case, the errormay simply be used to generate a notification of an uncorrectable error.

In a more complex example, it may be determined that there is aninconsistency between an entity's current state in a temporal listing ofaction terms of the existing recipe with regard to an entity, e.g., therecipe states to whip the potatoes, however the potatoes have not beenpreviously rendered into a “whippable” form, e.g., by converting them toa liquid or mixture state, which may require a series of operations,such as chopping the potatoes and cooking them to render them into a“softened” state which can be “whipped”.

A knowledge base of instructions may be searched based on the entity anda desired state, as determined from the pre-condition feature of theaction term. Thus, for example, if the entity is potatoes and the actionterm is “whip” and the constraint on the action term is “liquid” or“mixture” state, then the knowledge base of instructions may be searchedfor entries that result in a liquid or mixture state of potatoes, e.g.,“chop the potatoes into small pieces and add the butter and the sourcream.” Corresponding features of these instructions may be retrieved aswell, such as ingredient listings and amounts of ingredients, which canthen be used to add to the existing recipe that is being modified toinclude the missing instructions.

In some illustrative embodiments, the identification of instruction gapsor missing instructions may be used to generate instruction sets forautomated systems and/or devices, such as automated cookingsystems/devices, automated manufacturing systems/devices, automatedlaboratory test systems/devices, and the like. In some illustrativeembodiments, these instructions are provided to robotic devices thatperform actions on entities in accordance with the providedinstructions.

For example, a robotic system or device may be provided with an initialsparsely populated set of instructions to achieve a desired objective,e.g., generate a food item according to a recipe, generate an objectaccording to a set of manufacturing instructions, perform a specifictest on an entity based on a set of instructions, or the like. Based onthis sparsely populated set of instructions, a dynamic determination ofthe missing intervening instructions, corresponding to the instructiongaps, may be automatically performed and corresponding instructions forfilling the gaps may be automatically generated or selected based onanalysis of the attributes of entities and recognizable action terms inthe sparsely populated set of instructions. The mechanisms of theillustrative embodiments thus, “fill in the blanks” and provide thenecessary additional instructions to instruct the automated system toperform the missing operations to achieve the desired result.

Thus, the illustrative embodiments provide a mechanisms for expandingthe knowledge base or ontology that is used to perform cognitiveoperations. The illustrative embodiments may further provide mechanismsfor training/testing of human being with regard to an operationcomprising a temporally ordered series of tasks. Furthermore, theillustrative embodiments provide mechanisms for automatically correctingor augmenting a set of instructions which may be present in a document,in a set of instructions being provided to a human being, a set ofinstructions being provided to an automated system, or the like. All ofthese mechanisms utilize a bootstrapping mechanism that allows for alarge expanse of knowledge to be automatically generated and utilizedbased on an initial relatively small set of manually annotated termsspecifying constraints on entities.

Before beginning the discussion of the various aspects of theillustrative embodiments in more detail, it should first be appreciatedthat throughout this description the term “mechanism” is used to referto elements of the present invention that perform various operations,functions, and the like. A “mechanism,” as the term is used herein, maybe an implementation of the functions or aspects of the illustrativeembodiments in the form of an apparatus, a procedure, or a computerprogram product. In the case of a procedure, the procedure isimplemented by one or more devices, apparatus, computers, dataprocessing systems, or the like. In the case of a computer programproduct, the logic represented by computer code or instructions embodiedin or on the computer program product is executed by one or morehardware devices in order to implement the functionality or perform theoperations associated with the specific “mechanism.” Thus, themechanisms described herein may be implemented as specialized hardware,software executing on general purpose hardware, software instructionsstored on a medium such that the instructions are readily executable byspecialized or general purpose hardware, a procedure or method forexecuting the functions, or a combination of any of the above.

The present description and claims may make use of the terms “a”, “atleast one of”, and “one or more of” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within thescope of the description and claims.

Moreover, it should be appreciated that the use of the term “engine,” ifused herein with regard to describing embodiments and features of theinvention, is not intended to be limiting of any particularimplementation for accomplishing and/or performing the actions, steps,processes, etc., attributable to and/or performed by the engine. Anengine may be, but is not limited to, software, hardware and/or firmwareor any combination thereof that performs the specified functionsincluding, but not limited to, any use of a general and/or specializedprocessor in combination with appropriate software loaded or stored in amachine readable memory and executed by the processor. Further, any nameassociated with a particular engine is, unless otherwise specified, forpurposes of convenience of reference and not intended to be limiting toa specific implementation. Additionally, any functionality attributed toan engine may be equally performed by multiple engines, incorporatedinto and/or combined with the functionality of another engine of thesame or different type, or distributed across one or more engines ofvarious configurations.

In addition, it should be appreciated that the present description usesa plurality of various examples for various elements of the illustrativeembodiments to further illustrate example implementations of theillustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples intended tobe non-limiting and are not exhaustive of the various possibilities forimplementing the mechanisms of the illustrative embodiments. It will beapparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the examples provided herein without departing from thespirit and scope of the present invention.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

As noted above, the present invention provides mechanisms forimplementing an automated bootstrap based learning process to expand aknowledge base or ontology and utilizing that knowledge base or ontologyto perform cognitive operations. These cognitive operations, in someillustrative embodiments, may involve training/testing of human beingswith regard to a process comprising a series of tasks to generate adesired objective or result, the automated correction/augmenting of setsof instructions, the generation of instruction sets for automatedsystems/devices, and the like. In some cases, multi-media monitoring ofhuman beings and/or automated systems may be performed dynamically withdynamic response from the cognitive system to correct actions beingtaken by the human being/automated system. Various other types ofcognitive operations may also be implemented without departing from thespirit and scope of the present invention.

The illustrative embodiments may be utilized in many different types ofdata processing environments. In order to provide a context for thedescription of the specific elements and functionality of theillustrative embodiments, FIGS. 1-3 are provided hereafter as exampleenvironments in which aspects of the illustrative embodiments may beimplemented. It should be appreciated that FIGS. 1-3 are only examplesand are not intended to assert or imply any limitation with regard tothe environments in which aspects or embodiments of the presentinvention may be implemented. Many modifications to the depictedenvironments may be made without departing from the spirit and scope ofthe present invention.

FIGS. 1-3 are directed to describing an example cognitive system forperforming a cognitive operation based on knowledge gathered through abootstrapped automated learning process in accordance with theillustrative embodiments. In the depicted example, the cognitive systemimplements a request processing pipeline, such as a Question Answering(QA) pipeline (also referred to as a Question/Answer pipeline orQuestion and Answer pipeline) for example, request processingmethodology, and request processing computer program product with whichthe mechanisms of the illustrative embodiments are implemented. Theserequests may be provided as structure or unstructured request messages,natural language questions, or any other suitable format for requestingan operation to be performed by the cognitive system. As described inmore detail hereafter, the particular application that is implemented inthe cognitive system of the present invention is an application forevaluating an ordered set of tasks performed on, with, or to, entities,where the tasks and entities are domain specific and the form of thespecification of these ordered sets of tasks is also implementationspecific, e.g., electronic documents, monitored actions from monitoringequipment, or the like.

It should be appreciated that the cognitive system, while shown ashaving a single request processing pipeline in the examples hereafter,may in fact have multiple request processing pipelines. Each requestprocessing pipeline may be separately trained and/or configured toprocess requests associated with different domains or be configured toperform the same or different analysis on input requests (or questionsin implementations using a QA pipeline), depending on the desiredimplementation. For example, in some cases, a first request processingpipeline may be trained to operate on input requests directed to agenerating/correcting, or otherwise providing cooking recipes. In othercases, for example, the request processing pipelines may be configuredto provide different types of cognitive functions or support differenttypes of applications, such as one request processing pipeline beingused for evaluating the performance of operations for generating a fooditem based on a cooking recipe, training/testing a human being regardingthe performance of operations to generate such a food item based on acooking recipe, identifying instruction gaps and automatically fillingin those gaps with appropriate instructions, etc.

Moreover, each request processing pipeline may have their own associatedcorpus or corpora that they ingest and operate on, e.g., one corpus forcooking domain documents (e.g., comprising recipes and informationspecifying ingredients and/or actions associated with the cookingdomain), another corpus for manufacturing domain related documents formanufacturing a specific object, a third corpus for medical laboratorytest domain related documents, etc. In some cases, the requestprocessing pipelines may each operate on the same domain of inputquestions but may have different configurations, e.g., differentannotators or differently trained annotators, such that differentanalysis and potential answers are generated. The cognitive system mayprovide additional logic for routing input questions to the appropriaterequest processing pipeline, such as based on a determined domain of theinput request, combining and evaluating final results generated by theprocessing performed by multiple request processing pipelines, and othercontrol and interaction logic that facilitates the utilization ofmultiple request processing pipelines.

As noted above, one type of request processing pipeline with which themechanisms of the illustrative embodiments may be utilized is a QuestionAnswering (QA) pipeline. The description of example embodiments of thepresent invention hereafter will utilize a QA pipeline as an example ofa request processing pipeline that may be augmented to includemechanisms in accordance with one or more illustrative embodiments. Itshould be appreciated that while the present invention will be describedin the context of the cognitive system implementing one or more QApipelines that operate on an input question, the illustrativeembodiments are not limited to such. Rather, the mechanisms of theillustrative embodiments may operate on requests that are not posed as“questions” but are formatted as requests for the cognitive system toperform cognitive operations on a specified set of input data using theassociated corpus or corpora and the specific configuration informationused to configure the cognitive system. For example, rather than askinga natural language question of “What is a recipe for making a raspberrycheesecake?”, the cognitive system may instead receive a request of“generate a recipe for making a raspberry cheesecake,” or the like. Itshould be appreciated that the mechanisms of the QA system pipeline mayoperate on requests in a similar manner to that of input naturallanguage questions with minor modifications. In fact, in some cases, arequest may be converted to a natural language question for processingby the QA system pipelines if desired for the particular implementation.

As will be discussed in greater detail hereafter, the illustrativeembodiments may be integrated in, augment, and extend the functionalityof these QA pipeline, or request processing pipeline, mechanisms of acognitive system with regard to performing automated bootstrappedlearning of attributes of entities in a knowledge base or ontology. Theautomated mechanisms utilize an inference based learning methodology toexpand the knowledge base and ontology as outlined above and discussedhereafter. The automatically expanded knowledge base or ontology maythen be used to perform a cognitive operation.

Thus, it is important to first have an understanding of how cognitivesystems and question and answer creation in a cognitive systemimplementing a QA pipeline is implemented before describing how themechanisms of the illustrative embodiments are integrated in and augmentsuch cognitive systems and request processing pipeline, or QA pipeline,mechanisms. It should be appreciated that the mechanisms described inFIGS. 1-3 are only examples and are not intended to state or imply anylimitation with regard to the type of cognitive system mechanisms withwhich the illustrative embodiments are implemented. Many modificationsto the example cognitive system shown in FIGS. 1-3 may be implemented invarious embodiments of the present invention without departing from thespirit and scope of the present invention.

As an overview, a cognitive system is a specialized computer system, orset of computer systems, configured with hardware and/or software logic(in combination with hardware logic upon which the software executes) toemulate human cognitive functions. These cognitive systems applyhuman-like characteristics to conveying and manipulating ideas which,when combined with the inherent strengths of digital computing, cansolve problems with high accuracy and resilience on a large scale. Acognitive system performs one or more computer-implemented cognitiveoperations that approximate a human thought process as well as enablepeople and machines to interact in a more natural manner so as to extendand magnify human expertise and cognition. A cognitive system comprisesartificial intelligence logic, such as natural language processing (NLP)based logic, for example, and machine learning logic, which may beprovided as specialized hardware, software executed on hardware, or anycombination of specialized hardware and software executed on hardware.The logic of the cognitive system implements the cognitive operation(s),examples of which include, but are not limited to, question answering,identification of related concepts within different portions of contentin a corpus, intelligent search algorithms, such as Internet web pagesearches, for example, medical diagnostic and treatment recommendations,and other types of recommendation generation, e.g., items of interest toa particular user, potential new contact recommendations, or the like.

IBM Watson™ is an example of one such cognitive system which can processhuman readable language and identify inferences between text passageswith human-like high accuracy at speeds far faster than human beings andon a larger scale. In general, such cognitive systems are able toperform the following functions:

-   -   Navigate the complexities of human language and understanding    -   Ingest and process vast amounts of structured and unstructured        data    -   Generate and evaluate hypothesis    -   Weigh and evaluate responses that are based only on relevant        evidence    -   Provide situation-specific advice, insights, and guidance    -   Improve knowledge and learn with each iteration and interaction        through machine learning processes    -   Enable decision making at the point of impact (contextual        guidance)    -   Scale in proportion to the task    -   Extend and magnify human expertise and cognition    -   Identify resonating, human-like attributes and traits from        natural language    -   Deduce various language specific or agnostic attributes from        natural language    -   High degree of relevant recollection from data points (images,        text, voice) (memorization and recall)    -   Predict and sense with situational awareness that mimic human        cognition based on experiences    -   Answer questions based on natural language and specific evidence

In one aspect, cognitive systems provide mechanisms for answeringquestions posed to these cognitive systems using a Question Answeringpipeline or system (QA system) and/or process requests which may or maynot be posed as natural language questions. The QA pipeline or system isan artificial intelligence application executing on data processinghardware that answers questions pertaining to a given subject-matterdomain presented in natural language. The QA pipeline receives inputsfrom various sources including input over a network, a corpus ofelectronic documents or other data, data from a content creator,information from one or more content users, and other such inputs fromother possible sources of input. Data storage devices store the corpusof data. A content creator creates content in a document for use as partof a corpus of data with the QA pipeline. The document may include anyfile, text, article, or source of data for use in the QA system. Forexample, a QA pipeline accesses a body of knowledge about the domain, orsubject matter area, e.g., financial domain, medical domain, legaldomain, etc., where the body of knowledge (knowledgebase) can beorganized in a variety of configurations, e.g., a structured repositoryof domain-specific information, such as ontologies, or unstructured datarelated to the domain, or a collection of natural language documentsabout the domain.

Content users input questions to cognitive system which implements theQA pipeline. The QA pipeline then answers the input questions using thecontent in the corpus of data by evaluating documents, sections ofdocuments, portions of data in the corpus, or the like. When a processevaluates a given section of a document for semantic content, theprocess can use a variety of conventions to query such document from theQA pipeline, e.g., sending the query to the QA pipeline as a well-formedquestion which is then interpreted by the QA pipeline and a response isprovided containing one or more answers to the question. Semanticcontent is content based on the relation between signifiers, such aswords, phrases, signs, and symbols, and what they stand for, theirdenotation, or connotation. In other words, semantic content is contentthat interprets an expression, such as by using Natural LanguageProcessing.

As will be described in greater detail hereafter, the QA pipelinereceives an input question, parses the question to extract the majorfeatures of the question, uses the extracted features to formulatequeries, and then applies those queries to the corpus of data. Based onthe application of the queries to the corpus of data, the QA pipelinegenerates a set of hypotheses, or candidate answers to the inputquestion, by looking across the corpus of data for portions of thecorpus of data that have some potential for containing a valuableresponse to the input question. The QA pipeline then performs deepanalysis on the language of the input question and the language used ineach of the portions of the corpus of data found during the applicationof the queries using a variety of reasoning algorithms. There may behundreds or even thousands of reasoning algorithms applied, each ofwhich performs different analysis, e.g., comparisons, natural languageanalysis, lexical analysis, or the like, and generates a score. Forexample, some reasoning algorithms may look at the matching of terms andsynonyms within the language of the input question and the foundportions of the corpus of data. Other reasoning algorithms may look attemporal or spatial features in the language, while others may evaluatethe source of the portion of the corpus of data and evaluate itsveracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the QA pipeline. The statisticalmodel is used to summarize a level of confidence that the QA pipelinehas regarding the evidence that the potential response, i.e. candidateanswer, is inferred by the question. This process is repeated for eachof the candidate answers until the QA pipeline identifies candidateanswers that surface as being significantly stronger than others andthus, generates a final answer, or ranked set of answers, for the inputquestion.

As mentioned above, QA pipeline mechanisms operate by accessinginformation from a corpus of data or information (also referred to as acorpus of content), analyzing it, and then generating answer resultsbased on the analysis of this data. Accessing information from a corpusof data typically includes: a database query that answers questionsabout what is in a collection of structured records, and a search thatdelivers a collection of document links in response to a query against acollection of unstructured data (text, markup language, etc.).Conventional question answering systems are capable of generatinganswers based on the corpus of data and the input question, verifyinganswers to a collection of questions for the corpus of data, correctingerrors in digital text using a corpus of data, and selecting answers toquestions from a pool of potential answers, i.e. candidate answers.

Content creators, such as article authors, electronic document creators,web page authors, document database creators, and the like, determineuse cases for products, solutions, and services described in suchcontent before writing their content. Consequently, the content creatorsknow what questions the content is intended to answer in a particulartopic addressed by the content. Categorizing the questions, such as interms of roles, type of information, tasks, or the like, associated withthe question, in each document of a corpus of data allows the QApipeline to more quickly and efficiently identify documents containingcontent related to a specific query. The content may also answer otherquestions that the content creator did not contemplate that may beuseful to content users. The questions and answers may be verified bythe content creator to be contained in the content for a given document.These capabilities contribute to improved accuracy, system performance,machine learning, and confidence of the QA pipeline. Content creators,automated tools, or the like, annotate or otherwise generate metadatafor providing information useable by the QA pipeline to identify thesequestion and answer attributes of the content.

Operating on such content, the QA pipeline generates answers for inputquestions using a plurality of intensive analysis mechanisms whichevaluate the content to identify the most probable answers, i.e.candidate answers, for the input question. The most probable answers areoutput as a ranked listing of candidate answers ranked according totheir relative scores or confidence measures calculated duringevaluation of the candidate answers, as a single final answer having ahighest ranking score or confidence measure, or which is a best match tothe input question, or a combination of ranked listing and final answer.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of acognitive system 100 implementing a request processing pipeline 108,which in some embodiments may be a question answering (QA) pipeline, ina computer network 102. For purposes of the present description, it willbe assumed that the request processing pipeline 108 is implemented as aQA pipeline that operates on structured and/or unstructured requests inthe form of input questions. One example of a question processingoperation which may be used in conjunction with the principles describedherein is described in U.S. Patent Application Publication No.2011/0125734, which is herein incorporated by reference in its entirety.The cognitive system 100 is implemented on one or more computing devices104A-D (comprising one or more processors and one or more memories, andpotentially any other computing device elements generally known in theart including buses, storage devices, communication interfaces, and thelike) connected to the computer network 102. For purposes ofillustration only, FIG. 1 depicts the cognitive system 100 beingimplemented on computing device 104A only, but as noted above thecognitive system 100 may be distributed across multiple computingdevices, such as a plurality of computing devices 104A-D.

The network 102 includes multiple computing devices 104A-D, which mayoperate as server computing devices, and 110-112 which may operate asclient computing devices, in communication with each other and withother devices or components via one or more wired and/or wireless datacommunication links, where each communication link comprises one or moreof wires, routers, switches, transmitters, receivers, or the like. Insome illustrative embodiments, the cognitive system 100 and network 102enables question processing and answer generation (QA) functionality forone or more cognitive system users via their respective computingdevices 110-112. In other embodiments, the cognitive system 100 andnetwork 102 may provide other types of cognitive operations including,but not limited to, request processing and cognitive response generationwhich may take many different forms depending upon the desiredimplementation, e.g., cognitive information retrieval,training/instruction of users, cognitive evaluation of data, or thelike. Other embodiments of the cognitive system 100 may be used withcomponents, systems, sub-systems, and/or devices other than those thatare depicted herein.

The cognitive system 100 is configured to implement a request processingpipeline 108 that receive inputs from various sources. The requests maybe posed in the form of a natural language question, natural languagerequest for information, natural language request for the performance ofa cognitive operation, or the like. For example, the cognitive system100 receives input from the network 102, a corpus or corpora ofelectronic documents 106, cognitive system users, and/or other data andother possible sources of input. In one embodiment, some or all of theinputs to the cognitive system 100 are routed through the network 102.The various computing devices 104A-D on the network 102 include accesspoints for content creators and cognitive system users. Some of thecomputing devices 104A-D include devices for a database storing thecorpus or corpora of data 106 (which is shown as a separate entity inFIG. 1 for illustrative purposes only). Portions of the corpus orcorpora of data 106 may also be provided on one or more other networkattached storage devices, in one or more databases, or other computingdevices not explicitly shown in FIG. 1. The network 102 includes localnetwork connections and remote connections in various embodiments, suchthat the cognitive system 100 may operate in environments of any size,including local and global, e.g., the Internet.

In one embodiment, the content creator creates content in a document ofthe corpus or corpora of data 106 for use as part of a corpus of datawith the cognitive system 100. The document includes any file, text,article, or source of data for use in the cognitive system 100.Cognitive system users access the cognitive system 100 via a networkconnection or an Internet connection to the network 102, and inputquestions/requests to the cognitive system 100 that areanswered/processed based on the content in the corpus or corpora of data106. In one embodiment, the questions/requests are formed using naturallanguage. The cognitive system 100 parses and interprets thequestion/request via a pipeline 108, and provides a response to thecognitive system user, e.g., cognitive system user 110, containing oneor more answers to the question posed, response to the request, resultsof processing the request, or the like. In some embodiments, thecognitive system 100 provides a response to users in a ranked list ofcandidate answers/responses while in other illustrative embodiments, thecognitive system 100 provides a single final answer/response or acombination of a final answer/response and ranked listing of othercandidate answers/responses.

The cognitive system 100 implements the pipeline 108 which comprises aplurality of stages for processing an input question/request based oninformation obtained from the corpus or corpora of data 106. Thepipeline 108 generates answers/responses for the input question orrequest based on the processing of the input question/request and thecorpus or corpora of data 106. The pipeline 108 will be described ingreater detail hereafter with regard to FIG. 3.

In some illustrative embodiments, the cognitive system 100 may be theIBM Watson™ cognitive system available from International BusinessMachines Corporation of Armonk, N.Y., which is augmented with themechanisms of the illustrative embodiments described hereafter. Asoutlined previously, a pipeline of the IBM Watson™ cognitive systemreceives an input question or request which it then parses to extractthe major features of the question/request, which in turn are then usedto formulate queries that are applied to the corpus or corpora of data106. Based on the application of the queries to the corpus or corpora ofdata 106, a set of hypotheses, or candidate answers/responses to theinput question/request, are generated by looking across the corpus orcorpora of data 106 for portions of the corpus or corpora of data 106(hereafter referred to simply as the corpus 106) that have somepotential for containing a valuable response to the inputquestion/response (hereafter assumed to be an input question). Thepipeline 108 of the IBM Watson™ cognitive system then performs deepanalysis on the language of the input question and the language used ineach of the portions of the corpus 106 found during the application ofthe queries using a variety of reasoning algorithms.

The scores obtained from the various reasoning algorithms are thenweighted against a statistical model that summarizes a level ofconfidence that the pipeline 108 of the IBM Watson™ cognitive system100, in this example, has regarding the evidence that the potentialcandidate answer is inferred by the question. This process is berepeated for each of the candidate answers to generate ranked listing ofcandidate answers which may then be presented to the user that submittedthe input question, e.g., a user of client computing device 110, or fromwhich a final answer is selected and presented to the user. Moreinformation about the pipeline 108 of the IBM Watson™ cognitive system100 may be obtained, for example, from the IBM Corporation website, IBMRedbooks, and the like. For example, information about the pipeline ofthe IBM Watson™ cognitive system can be found in Yuan et al., “Watsonand Healthcare,” IBM developerWorks, 2011 and “The Era of CognitiveSystems: An Inside Look at IBM Watson and How it Works” by Rob High, IBMRedbooks, 2012.

As noted above, while the input to the cognitive system 100 from aclient device may be posed in the form of a natural language question,the illustrative embodiments are not limited to such. Rather, the inputquestion may in fact be formatted or structured as any suitable type ofrequest which may be parsed and analyzed using structured and/orunstructured input analysis, including but not limited to the naturallanguage parsing and analysis mechanisms of a cognitive system such asIBM Watson™, to determine the basis upon which to perform cognitiveanalysis and providing a result of the cognitive analysis. In the caseof a cooking domain implementation, for example, the request may be totrain/test a human being to perform tasks for generating a food itemcorresponding to a recipe, to generate a recipe, or the like, such thatthe analysis performed may be with regard to recipe instructions,ingredient attributes, and action term features, for example.

In the context of the present invention, cognitive system 100 mayprovide a cognitive functionality for providing an ordered set of tasksfor generating a desired result or achieving a particular objective. Inthe context of a cognitive system such as IBM Chef Watson™, for example,the cognitive functionality may be the generation of recipes, correctionor augmentation of recipes, the training/testing of human beings withregard to recipe preparation, dynamic feedback based on dynamicallyobtained information about actions of human beings or automated systemsto perform actions to prepare a recipe, or the like. The cognitivefunctionality may make use of the automated bootstrapped learningprocess mechanisms of the illustrative embodiments to perform suchcognitive operations.

As shown in FIG. 1, the cognitive system 100 is further augmented, inaccordance with the mechanisms of the illustrative embodiments, toinclude logic implemented in specialized hardware, software executed onhardware, or any combination of specialized hardware and softwareexecuted on hardware, for implementing a knowledge acquisition system150 which performs operations for automatically expanding a knowledgebase 160 comprising ontology 162 based on an automated bootstrap basedlearning process. That is, as noted above, the knowledge acquisitionsystem 150 comprises bootstrap learning process logic 152 andconfiguration data structures 154 which operate on seed action terminput 170 and domain specific attribute learning configuration datastructures 175 to expand the knowledge base 160, e.g., ontology 162 inknowledge base 160, based on processing of documents in a domainspecific corpus 106.

The knowledge acquisition system 150 may utilize language processingresources 156 and natural language processing (NLP) logic 158 to analyzethe documents of a domain specific corpus 106 to extract the informationneeded to perform the bootstrap learning process. While FIG. 1 showsthese resources 156 and NLP logic 158 as separate from the cognitivesystem pipeline 108, in some illustrative embodiments, the bootstraplearning process logic 152 may utilize the already provided logic andresources available in the cognitive system 100 and/or pipeline 108 toperform its bootstrap learning process.

Based on the results of the processing of the documents in the corpus106, the expanded knowledge base 160 may be provided to the cognitivesystem 100 for use in performing its cognitive operations. Morespecifically, the pipeline 108 of the cognitive system 100 may utilizethe knowledge base 160 and/or ontology 162 as a basis for evaluating aninput request or question and generate a corresponding response/answer.

As noted above, the bootstrap learning process logic 152 of theknowledge acquisition system 150 performs a learning process forbootstrapping a small amount of manually acquired domain knowledge, suchas specified in the seed action terms 170, to automatically build a muchlarger set of domain knowledge, such as in the knowledge base 160 and/orontology 162, where the knowledge acquired is inferred from documents ofa corpus 106 based on the usage constraints of the seed action terms 170and their instances of being performed on domain specific entities inthe documents of the corpus 106. In the example implementations, theserelationships are entity/action pairs where the entities are expressedin natural language content of the documents in corpus 106 as nouns,while the actions are represented in the natural language content asverbs, thus an entity/action pair may also be represented as noun/verbpairs.

It should be appreciated that the seed action terms data structure 170comprises a relatively small set of terms than represents entities inthe knowledge base 160 and/or ontology 162, e.g., 500 terms in the seedaction terms data structure 170 as opposed to over 4000 termsrepresenting entities in the knowledge base 160 and/or ontology 162.Moreover, the action terms specified in the seed action terms datastructure 170 may be manually tagged with features that indicateconstraints on the attributes of entities with which that correspondingaction may be utilized, i.e. pre-condition features. In someillustrative embodiments, the seed action terms may also be manuallytagged with features that indicate the result of the operation of thecorresponding action on an entity that satisfies the constraintrequirement, i.e. post-condition features. For example, in a cookingdomain, the seed action terms 170 may represent actions that can beperformed on ingredients, or combinations of ingredients. The actionterms corresponding to these actions may be manually tagged withpre-condition features specifying a state of matter attribute constraintfor the entities upon which the corresponding action may be performed,and post-condition features indicating a state of matter attribute thatresults from the operation of the action on an entity satisfying acorresponding pre-condition feature.

In addition to the seed action terms data structure 170, a domainspecific attribute learning configuration data structure 175 may bemanually specified. The domain specific attribute learning configurationdata structure 175 specifies one or more attributes of entities that areto be learned by an automated bootstrap learning process implemented bythe knowledge acquisition system 150. For example, the domain specificattribute learning configuration data structure 175 may specify that thebootstrap learning process logic 152 of the knowledge acquisition system150 is to operate to learn a state of matter attribute of entities inthe knowledge base 160 and/or ontology 162, for example.

The relatively small set of action terms in the seed action terms datastructure 170, as well as the domain specific attribute learningconfiguration data structure 175 specifying the attributes for theentities that are to be learned, are input to the knowledge acquisitionsystem 150 to thereby configure the knowledge acquisition system 150 toperform automated bootstrap based learning of domain specific attributesfor entities in the knowledge base 160 and/or ontology 162. Thisconfiguration may be stored in configuration data structure(s) 154. Theconfiguration data structure(s) 154 may also store other configurationparameters and data which may be required by the knowledge acquisitionsystem 150 and/or bootstrap learning process logic 152 to performautomated bootstrap based learning of the specified attribute(s) in thedomain specific attribute learning configuration data structure 175. Forexample, these configuration data structure(s) 154 may specify one ormore threshold confidence scores against which evidence based confidencescore values generated for various values of the attribute may becompared to determine which values of the attribute to maintain inassociation with the entities.

The knowledge acquisition system 150 further receives as input aknowledge base 160 and/or ontology 162 that specifies the entities withwhich the bootstrap learning process logic 152 operates and for whichthe attributes specified in the domain specific attribute learningconfiguration data structure 175 are to be learned. The generation ofontologies of entities or knowledge bases of entities is generally knownin the art and thus, a more detailed description is not provided herein.In general, the knowledge base 160 and/or ontology 162 comprises datastructures for entities and specifies relationships between theseentities, such as in the form of a directed acyclic graph or other suchhierarchical and/or relational data structure. The knowledge base 160 orontology 162 may be manually, automatically, or semi-automaticallygenerated and operates as a seed set of entities for which knowledge isto be acquired by the knowledge acquisition system 150. The knowledgebase 160 and/or ontology 162 may also serve as a basis upon which thecognitive system 100 performs its cognitive operations once expanded bythe mechanisms of the illustrative embodiments.

With the seed action terms data structure 170 specifying the seed actionterms and their constraint/post-condition features, the domain specificattribute learning configuration data structure 175 specifying theattribute(s) to be learned for the entities, the knowledge base 160and/or ontology 162 providing the recognized entities for which theattribute(s) are to be learned, and the configuration data structure(s)154 specifying configuration parameters for the bootstrap learningprocess logic 152 of the knowledge acquisition system 150, the knowledgeacquisition system 150 processes documents of a corpus 106 to generatethe attribute values for the entities in the knowledge base 160 and/orontology 162 via an automated bootstrap based learning processimplemented by the logic 152. This automated learning involves the useof natural language processing logic 158 operating on the naturallanguage content of the documents in the domain specific corpus 106,using domain specific natural language processing resources 156. Thesedomain specific natural language processing resources 156 may compriseany resources needed to facilitate natural language processing onnatural language content and may include, but is not limited to,dictionaries, synonym mapping data structures, named entityidentification resources, and the like. The natural language processinglogic 158 extracts elements from the natural language content ofdocuments of the corpus 106 which may include key terms and/or phrasesassociated with domain specific entities and actions, such as maycorrespond to action terms in the seed action terms data structure 170and entities in the knowledge base 160 and/or ontology 162.

In accordance with the illustrative embodiments, using a single documentof the corpus 106 as an example, the natural language processing logic158 analyzes the natural language content of a document and provides theextracted elements to the bootstrap learning process logic 152preserving the order in which these elements appear in the document. Thebootstrap learning process logic 152 operates to infer the attributes ofentities by observing the use of the action terms in conjunction withthe entities in the natural language content, e.g., a particularingredient is an argument of that action term, with the inferredattributes of entities being generated from the analysis of the usage ofaction terms in conjunction with an entity term for the entity and thepre-condition features associated with those action terms. The resultingvalues for the attribute may then be added to an entry for the entity inthe knowledge base 160 and/or ontology 162, such as by populating avalue in an attribute field of the entity entry. In this way, theknowledge base 160 and/or ontology 162 for the cognitive system 100 isautomatically expanded through cognitive analysis of natural languagecontent of documents in the corpus 106 using a small initial set oftagged action terms 170.

For example, the bootstrap learning process logic 152 may arrange therecognized action terms, associated with an entity specified in theknowledge base 160 and/or ontology 162, found in the natural languagecontent of the document (via the natural language processing performedby logic 158), into a correct temporal ordering to thereby generate atemporally ordered action term listing associated with the entity. Asnoted above, this temporal ordering may be based on the ordering inwhich the action terms appear in the document, an ordering based on morecomplex analysis of temporal terms set forth in the natural languagecontent of the document, or the like. This process may be performed fora plurality of entities referenced in the document such that a separatetemporally ordered action term listing is generated for each identifiedentity.

With the action terms associated with an entity having been orderedaccording to temporal characteristics, the first action term thatoperates on or references the entity of interest is identified by thebootstrap learning process logic 152 automated learning process whichassumes that the entity of interest satisfies the pre-condition featuresof the first action term that operates on the entity in the temporallyordered action term listing. The pre-condition feature of the firstaction term is then applied to the entity as a value of thecorresponding attribute of the entity, i.e. the entity is now recognizedas having the value associated with the attribute corresponding to thepre-condition feature, e.g., if the pre-condition feature requires asolid entity, then the value of a state of matter attribute of theentity is set to a “solid” value.

In one illustrative embodiment, this process is done with regard to eachdocument and each entity referenced by each document in the corpus 106.Thus, for each entity referenced in the corpus 106 a value of anattribute may be learned. In other illustrative embodiments, thebootstrap learning process logic 152 may maintain an attribute valuetracking data structure 156 for each value of the attribute to belearned for each entity. For example, if the attribute to be learned isa state of matter attribute, then for each entity the various valuesencountered for this attribute through processing of the documents maybe stored in an attribute value tracking data structure 156. Inaddition, a count of each time the value is encountered for thisattribute across all of the documents of the corpus 106 may bemaintained. Based on these counts, a confidence score may be calculatedfor each value of the attribute and compared to a threshold value by thebootstrap learning process logic 152. The threshold value may be aconfidence score threshold specified in the configuration data structure154, for example. Those values for the attribute that meet therequirements of the threshold may be maintained in the knowledge base160 and/or ontology 162 while others that do not may be discarded.

In addition, the bootstrap learning process logic 152 may generalize thenewly learned attribute values for the entity up a domain specificsubsumption hierarchy of the knowledge base 160 and/or ontology 162, sothat the attribute values may be associated with other entities andconcepts in the hierarchy of the knowledge base 160 and/or ontology.This propagated knowledge may then be used to reason about otherentities in the knowledge base 160 and/or ontology based on therelationships associated with the entities specified therein.

The result of this automated learning process is an expanded knowledgebase 160 and/or ontology 162 that is expanded with regard to theattributes of the entities for the specific domain. The expandedknowledge base may then be utilized as input to the cognitive system 100to perform various cognitive operations including, but not limited to,performing question answering or responding to requests for information,correction of natural language documents or text, expanding upon thecontent of a natural language document, training/testing of human users,automatic generation of instructions or commands for controlling theoperation of an automated system or device, performing monitoring ofhuman actions or interactions with entities and providing constructivefeedback or instruction, and/or the like.

As noted previously, in some illustrative embodiments in whichpost-condition features are associated with action terms in the seedaction terms data structure 170, the process of learning values forattributes of an entity does not stop at the first action term in thedocument referencing the entity. To the contrary, in furtherillustrative embodiments, the temporally ordered action term listing maybe followed with veracity checks of the pre-condition features of eachaction term being performed with regard to the value of the attributedgenerated by the temporally previous action term in the temporallyordered action term listing. For each action term in the temporallyordered action term listing where the veracity check succeeds,additional evidential support for a value of the attribute is identifiedand maintained in the attribute value tracking data structure and/or anew value of the attribute is recorded in association with the entity.If an action term's pre-condition feature is not satisfied by the valueof the attribute specified in the “post-condition” feature of a previousaction term, then the processing of action terms in the temporallyordered action term listing data structure 156 for this entity and thisdocument is discontinued.

The cognitive system 100 operates on the expanded knowledge base 160and/or ontology 162 to perform a cognitive operation, which may employthe use of pipeline 108, for example. In one illustrative embodiment,the cognitive system 100 operates to identify errors in a document, suchas a document present in the corpus 106 or another corpus upon which thelearning of attributes of entities was not based. For example, thecorpus 106 may represent an initial set of recipes in a cooking domainupon which the knowledge acquisition system 150 operates toautomatically learn the attribute values for a plurality of ingredients.Thereafter, the operation of the cognitive system 100 may be performedwith regard to new recipes that are not present in the corpus 106. Thesenew recipes may be those that are to be added to the corpus 106 forexample, generated by an automated system, such as the IBM Chef Watson™automated recipe system available from IBM Corporation of Armonk, N.Y.,or the like. The new recipes are processed for correctness prior toaddition to the corpus 106. In other illustrative embodiments, theserecipes may be those that are supplied by users, such as via clientcomputing devices 110, 112, or server computing devices 104B-104D, andfor which a request is received to verify the correctness of the recipe.For example, a document may be provided that includes recipes and thecognitive system 100 may apply the knowledge in the knowledge base 160and/or ontology to the recipe as well as the pre-condition featuresassociated with action terms to determine the correctness of therecipes. In some illustrative embodiments, the input of the recipe maybe in terms of responses from human users to prompts from the cognitivesystem 100. The identified errors may be correlated with corrections orinstructions to correct the content of the documents or instruct a humanbeing or automated system with regard to the correct temporal orderingof actions to be performed on an entity with regard to the particularobjective that is to be achieved, e.g., preparing a recipe,manufacturing an object, performing a laboratory test, or the like.

As noted above, the mechanisms of the illustrative embodiments arerooted in the computer technology arts and are implemented using logicpresent in such computing or data processing systems. These computing ordata processing systems are specifically configured, either throughhardware, software, or a combination of hardware and software, toimplement the various operations described above. As such, FIG. 2 isprovided as an example of one type of data processing system in whichaspects of the present invention may be implemented. Many other types ofdata processing systems may be likewise configured to specificallyimplement the mechanisms of the illustrative embodiments.

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments are implemented. Data processingsystem 200 is an example of a computer, such as server 104 or client 110in FIG. 1, in which computer usable code or instructions implementingthe processes for illustrative embodiments of the present invention arelocated. In one illustrative embodiment, FIG. 2 represents a servercomputing device, such as a server 104, which, which implements acognitive system 100 and QA system pipeline 108 augmented to include theadditional mechanisms of the illustrative embodiments describedhereafter.

In the depicted example, data processing system 200 employs a hubarchitecture including north bridge and memory controller hub (NB/MCH)202 and south bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 areconnected to NB/MCH 202. Graphics processor 210 is connected to NB/MCH202 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 212 connectsto SB/ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem222, read only memory (ROM) 224, hard disk drive (HDD) 226, CD-ROM drive230, universal serial bus (USB) ports and other communication ports 232,and PCI/PCIe devices 234 connect to SB/ICH 204 through bus 238 and bus240. PCI/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbasic input/output system (BIOS).

HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240. HDD226 and CD-ROM drive 230 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 236 is connected to SB/ICH 204.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within the dataprocessing system 200 in FIG. 2. As a client, the operating system is acommercially available operating system such as Microsoft® Windows 8°.An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 200.

As a server, data processing system 200 may be, for example, an IBM®eServer™ System p° computer system, running the Advanced InteractiveExecutive) (AIX® operating system or the LINUX® operating system. Dataprocessing system 200 may be a symmetric multiprocessor (SMP) systemincluding a plurality of processors in processing unit 206.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 226, and are loaded into main memory 208 for execution byprocessing unit 206. The processes for illustrative embodiments of thepresent invention are performed by processing unit 206 using computerusable program code, which is located in a memory such as, for example,main memory 208, ROM 224, or in one or more peripheral devices 226 and230, for example.

A bus system, such as bus 238 or bus 240 as shown in FIG. 2, iscomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 222 or network adapter 212 of FIG. 2, includes one or more devicesused to transmit and receive data. A memory may be, for example, mainmemory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG. 2.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIGS. 1 and 2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS. 1and 2. Also, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system, other than the SMPsystem mentioned previously, without departing from the spirit and scopeof the present invention.

Moreover, the data processing system 200 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 200 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 200 may be any known or later developed dataprocessing system without architectural limitation.

FIG. 3 illustrates an example of a cognitive system processing pipelinewhich, in the depicted example, is a question and answer (QA) systempipeline used to process an input question in accordance with oneillustrative embodiment. As noted above, the cognitive systems withwhich the illustrative embodiments may be utilized are not limited to QAsystems and thus, not limited to the use of a QA system pipeline. FIG. 3is provided only as one example of the processing structure that may beimplemented to process a natural language input requesting the operationof a cognitive system to present a response or result to the naturallanguage input.

The QA system pipeline of FIG. 3 may be implemented, for example, as QApipeline 108 of cognitive system 100 in FIG. 1. It should be appreciatedthat the stages of the QA pipeline shown in FIG. 3 are implemented asone or more software engines, components, or the like, which areconfigured with logic for implementing the functionality attributed tothe particular stage. Each stage is implemented using one or more ofsuch software engines, components or the like. The software engines,components, etc. are executed on one or more processors of one or moredata processing systems or devices and utilize or operate on data storedin one or more data storage devices, memories, or the like, on one ormore of the data processing systems. The QA pipeline of FIG. 3 isaugmented, for example, in one or more of the stages to implement theimproved mechanism of the illustrative embodiments described hereafter,additional stages may be provided to implement the improved mechanism,or separate logic from the pipeline 300 may be provided for interfacingwith the pipeline 300 and implementing the improved functionality andoperations of the illustrative embodiments.

As shown in FIG. 3, the QA pipeline 300 comprises a plurality of stages310-380 through which the cognitive system operates to analyze an inputquestion and generate a final response. In an initial question inputstage 310, the QA pipeline 300 receives an input question that ispresented in a natural language format. That is, a user inputs, via auser interface, an input question for which the user wishes to obtain ananswer, e.g., “Who are Washington's closest advisors?” In response toreceiving the input question, the next stage of the QA pipeline 300,i.e. the question and topic analysis stage 320, parses the inputquestion using natural language processing (NLP) techniques to extractmajor features from the input question, and classify the major featuresaccording to types, e.g., names, dates, or any of a plethora of otherdefined topics. For example, in the example question above, the term“who” may be associated with a topic for “persons” indicating that theidentity of a person is being sought, “Washington” may be identified asa proper name of a person with which the question is associated,“closest” may be identified as a word indicative of proximity orrelationship, and “advisors” may be indicative of a noun or otherlanguage topic.

In addition, the extracted major features include key words and phrasesclassified into question characteristics, such as the focus of thequestion, the lexical answer type (LAT) of the question, and the like.As referred to herein, a lexical answer type (LAT) is a word in, or aword inferred from, the input question that indicates the type of theanswer, independent of assigning semantics to that word. For example, inthe question “What maneuver was invented in the 1500s to speed up thegame and involves two pieces of the same color?,” the LAT is the string“maneuver.” The focus of a question is the part of the question that, ifreplaced by the answer, makes the question a standalone statement. Forexample, in the question “What drug has been shown to relieve thesymptoms of ADD with relatively few side effects?,” the focus is “drug”since if this word were replaced with the answer, e.g., the answer“Adderall” can be used to replace the term “drug” to generate thesentence “Adderall has been shown to relieve the symptoms of ADD withrelatively few side effects.” The focus often, but not always, containsthe LAT. On the other hand, in many cases it is not possible to infer ameaningful LAT from the focus.

Referring again to FIG. 3, the identified major features are then usedduring the question decomposition stage 330 to decompose the questioninto one or more queries that are applied to the corpora ofdata/information 345 in order to generate one or more hypotheses. Thequeries are generated in any known or later developed query language,such as the Structure Query Language (SQL), or the like. The queries areapplied to one or more databases storing information about theelectronic texts, documents, articles, websites, and the like, that makeup the corpora of data/information 345. That is, these various sourcesthemselves, different collections of sources, and the like, represent adifferent corpus 347 within the corpora 345. There may be differentcorpora 347 defined for different collections of documents based onvarious criteria depending upon the particular implementation. Forexample, different corpora may be established for different topics,subject matter categories, sources of information, or the like. As oneexample, a first corpus may be associated with healthcare documentswhile a second corpus may be associated with financial documents.Alternatively, one corpus may be documents published by the U.S.Department of Energy while another corpus may be IBM Redbooks documents.Any collection of content having some similar attribute may beconsidered to be a corpus 347 within the corpora 345.

The queries are applied to one or more databases storing informationabout the electronic texts, documents, articles, websites, and the like,that make up the corpus of data/information, e.g., the corpus of data106 in FIG. 1. The queries are applied to the corpus of data/informationat the hypothesis generation stage 340 to generate results identifyingpotential hypotheses for answering the input question, which can then beevaluated. That is, the application of the queries results in theextraction of portions of the corpus of data/information matching thecriteria of the particular query. These portions of the corpus are thenanalyzed and used, during the hypothesis generation stage 340, togenerate hypotheses for answering the input question. These hypothesesare also referred to herein as “candidate answers” for the inputquestion. For any input question, at this stage 340, there may behundreds of hypotheses or candidate answers generated that may need tobe evaluated.

The QA pipeline 300, in stage 350, then performs a deep analysis andcomparison of the language of the input question and the language ofeach hypothesis or “candidate answer,” as well as performs evidencescoring to evaluate the likelihood that the particular hypothesis is acorrect answer for the input question. As mentioned above, this involvesusing a plurality of reasoning algorithms, each performing a separatetype of analysis of the language of the input question and/or content ofthe corpus that provides evidence in support of, or not in support of,the hypothesis. Each reasoning algorithm generates a score based on theanalysis it performs which indicates a measure of relevance of theindividual portions of the corpus of data/information extracted byapplication of the queries as well as a measure of the correctness ofthe corresponding hypothesis, i.e. a measure of confidence in thehypothesis. There are various ways of generating such scores dependingupon the particular analysis being performed. In generally, however,these algorithms look for particular terms, phrases, or patterns of textthat are indicative of terms, phrases, or patterns of interest anddetermine a degree of matching with higher degrees of matching beinggiven relatively higher scores than lower degrees of matching.

Thus, for example, an algorithm may be configured to look for the exactterm from an input question or synonyms to that term in the inputquestion, e.g., the exact term or synonyms for the term “movie,” andgenerate a score based on a frequency of use of these exact terms orsynonyms. In such a case, exact matches will be given the highestscores, while synonyms may be given lower scores based on a relativeranking of the synonyms as may be specified by a subject matter expert(person with knowledge of the particular domain and terminology used) orautomatically determined from frequency of use of the synonym in thecorpus corresponding to the domain. Thus, for example, an exact match ofthe term “movie” in content of the corpus (also referred to as evidence,or evidence passages) is given a highest score. A synonym of movie, suchas “motion picture” may be given a lower score but still higher than asynonym of the type “film” or “moving picture show.” Instances of theexact matches and synonyms for each evidence passage may be compiled andused in a quantitative function to generate a score for the degree ofmatching of the evidence passage to the input question.

Thus, for example, a hypothesis or candidate answer to the inputquestion of “What was the first movie?” is “The Horse in Motion.” If theevidence passage contains the statements “The first motion picture evermade was ‘The Horse in Motion’ in 1878 by Eadweard Muybridge. It was amovie of a horse running,” and the algorithm is looking for exactmatches or synonyms to the focus of the input question, i.e. “movie,”then an exact match of “movie” is found in the second sentence of theevidence passage and a highly scored synonym to “movie,” i.e. “motionpicture,” is found in the first sentence of the evidence passage. Thismay be combined with further analysis of the evidence passage toidentify that the text of the candidate answer is present in theevidence passage as well, i.e. “The Horse in Motion.” These factors maybe combined to give this evidence passage a relatively high score assupporting evidence for the candidate answer “The Horse in Motion” beinga correct answer.

It should be appreciated that this is just one simple example of howscoring can be performed. Many other algorithms of various complexitymay be used to generate scores for candidate answers and evidencewithout departing from the spirit and scope of the present invention.

In the synthesis stage 360, the large number of scores generated by thevarious reasoning algorithms are synthesized into confidence scores orconfidence measures for the various hypotheses. This process involvesapplying weights to the various scores, where the weights have beendetermined through training of the statistical model employed by the QApipeline 300 and/or dynamically updated. For example, the weights forscores generated by algorithms that identify exactly matching terms andsynonym may be set relatively higher than other algorithms that areevaluating publication dates for evidence passages. The weightsthemselves may be specified by subject matter experts or learned throughmachine learning processes that evaluate the significance ofcharacteristics evidence passages and their relative importance tooverall candidate answer generation.

The weighted scores are processed in accordance with a statistical modelgenerated through training of the QA pipeline 300 that identifies amanner by which these scores may be combined to generate a confidencescore or measure for the individual hypotheses or candidate answers.This confidence score or measure summarizes the level of confidence thatthe QA pipeline 300 has about the evidence that the candidate answer isinferred by the input question, i.e. that the candidate answer is thecorrect answer for the input question.

The resulting confidence scores or measures are processed by a finalconfidence merging and ranking stage 370 which compares the confidencescores and measures to each other, compares them against predeterminedthresholds, or performs any other analysis on the confidence scores todetermine which hypotheses/candidate answers are the most likely to bethe correct answer to the input question. The hypotheses/candidateanswers are ranked according to these comparisons to generate a rankedlisting of hypotheses/candidate answers (hereafter simply referred to as“candidate answers”). From the ranked listing of candidate answers, atstage 380, a final answer and confidence score, or final set ofcandidate answers and confidence scores, are generated and output to thesubmitter of the original input question via a graphical user interfaceor other mechanism for outputting information.

As shown in FIG. 3, in accordance with one illustrative embodiment, thepipeline 300 operates in conjunction with a knowledge base 390 that is,at least in part, generated by the knowledge acquisition system 395through an automated bootstrap learning process based on a relativelysmall set of manually tagged action terms that can be used to infer thevalues of attributes for entities in the knowledge base 390 and therebyexpand an initial knowledge base into the knowledge base 390 used by thepipeline 300. The pipeline 108 may utilize the knowledge base 390 aspart of the evidence or information for generating hypotheses, orcandidate answers, and/or evaluating evidential support for hypothesesor candidate answers. In other illustrative embodiments, the pipelinemay process natural language content for determining errors in thenatural language content, missing content, or the like, and generatingcorrections for the errors, the missing content, or the like.

Thus, with the mechanism of the illustrative embodiments, automatedbootstrap based learning is performed with regard to domain specificattributes of domain specific entities based on a small seed set ofdomain specific action terms. The illustrative embodiments alleviate thehuman effort required to generate comprehensive domain specificknowledge bases or ontologies and reduces the likelihood of human errorbeing introduced into the knowledge base or ontology.

As noted above, in some illustrative embodiments, the cognitive systemis implemented in a cooking domain and provides cognitive logic toperform cognitive operations with regard to the generation, evaluation,and preparation of recipes. As such, the illustrative embodiments may beutilized to expand a cooking domain knowledge base including entitieswhich are ingredients of recipes and the relationships between suchentities, e.g., hierarchical representations of categories of entitiessuch as an entity for “root vegetables” with child entities being“potatoes,” “onions,” “turnips,” etc. The expansion of the cookingdomain knowledge base of ingredients may be performed by using a smallset of manually tagged action terms, evaluating recipes in which suchaction terms and references to entities of the knowledge base arepresent, and identifying values for attributes of the entities from thepre-condition features associated with the action terms.

To illustrate this process further consider the example recipe shown inFIG. 4. Assume with regard to this recipe example, that the knowledgeacquisition system 150 is attempting to learn a state of matterattribute of ingredients in a knowledge base, where the particularvalues of a state of matter attribute may be specified as a particularset of recognized values, e.g., solid, liquid, particulate, mixture,etc. Since the cooking domain is largely imperative, the automatedbootstrap learning process is primarily interested in the direct objectof an action term. Thus, action terms may be defined such as “cut”,“stir”, “sift”, “boil”, “melt”, “grate”, and the like. Each of theseaction terms may have a pre-condition feature and a “post-condition”feature defined manually for those action terms, such as by a humansubject matter expert. An example correlation of action term withfeatures may be as follows:

TABLE 1 Example of Action Terms and Features Action Term Pre-conditionFeature Post-condition Feature Cut Solid Solid Stir Not Solid Not SolidSift Particulate Particulate Boil Liquid Not Solid Melt Solid LiquidGrate Solid ParticulateIt should be appreciated that in addition to the mapping of action termswith pre-condition features and post-condition features, naturallanguage processing resources, such as synonym dictionaries or mappingdata structures that map synonyms to the action terms, may be definedand used by natural language processing logic when processing inputnatural language content, such as in electronic documents or portions ofelectronic documents.

Assuming that a document comprising the recipe in FIG. 4 is received asinput upon which the mechanisms of the illustrative embodiments are tooperate, the recipe is parsed and analyzed with natural languageprocessing logic to extract instances of recognizable action terms whichare then mapped to the corresponding pre-condition features andpost-condition features. In addition, the object of the action terms maybe identified via the natural language processing to thereby identifythe entities for which attributes may be learned from the action terms.It should be appreciated that synonyms may be identified through thenatural language processing and mapped to the action terms having theirpre-condition and post-condition features defined.

For each identified entity, a temporally ordered action term listing isgenerated. Thus, for example, with regard to the recipe of FIG. 4, atemporally ordered action term listing for the ingredient butter may beobtained as follows: Melt and then Drizzle. The first action term thatmodifies the entity “butter” is identified, i.e. melt, has apre-condition feature of “solid” (see Table 1 above). The pre-conditionfeature of the first action term in the temporally ordered action termlisting for the entity “butter” is transferred to the entity as a valuefor the attribute being learned, e.g., state of matter attribute=solid.Similarly, the action term “chop” in the recipe of FIG. 4 is a synonymfor “cut” and therefore, the pre-condition feature of “cut” may betransferred to the state of matter attribute for the ingredient entityof “onions,” thereby indicating that onions are solid. Likewise, theaction term “whisk” is a synonym for “stir” and thus, the pre-conditionfeature of “stir” may be transferred to the ingredient entity “milk”thereby indicating that milk is “not solid.”

The temporally ordered action terms in the listing for an entity may betraversed to ensure that each subsequent action term's pre-conditionfeature matches the “post-condition” feature of the previous actionterm. If the pre-condition feature of the next action term in thelisting matches the “post-condition” feature of the previous actionterm, then the pre-condition feature of the next action term may be usedas further evidence for a value of the attribute to be learned. Thus,for example, in the recipe for butter, it can be determined that thefirst action term, “melt”, post-condition a liquid and that thesubsequent action term of “drizzle” has a pre-condition feature ofliquid. Hence, there is a match between the post-condition feature ofthe first action term with the pre-condition feature of the secondaction term. Therefore, there is additional evidence that another valuefor the state of matter attribute of butter is “liquid.” Thus, knowledgeabout butter has been acquired indicating that butter may have a stateof matter of either solid or liquid.

As discussed above, this process may be repeated until there is adiscrepancy between the post-condition feature of a previous action termand a pre-condition feature of the current action term, or until allaction terms in the temporally ordered action term listing for an entityhave been processed. Through this iterative process, evidence for eachvalue of the learned attribute is accumulated, e.g., counts of instancesof a value of the learned attribute may be accumulated. This process maythen also be repeated across multiple documents, portions of documents,or the like, e.g., across multiple recipes, to accumulate evidence foreach value of the learned attribute.

Confidence scores may then be calculated for each value of the learnedattribute based on the accumulated evidence. The confidence scores maybe compared to a predetermined threshold value and, for each value whoseconfidence score meets or exceeds the predetermined threshold, thecorresponding value of the learned attribute is maintained inassociation with the entity as a valid value for the attribute for thatentity. For example, if there is sufficient evidence that butter has astate of matter attribute value of “solid” and a state of matterattribute value of “liquid” then both values will be maintained inassociation with the entity, e.g., added to attribute data for theentity in the entity's data structure.

The values of the attributes maintained for an entity may be generalizedup a subsumption hierarchy of the ontology. For example, it may bedetermined that onions, potatoes, and turnips all have substantialevidence of having a state of matter attribute of “solid.” Thus, it maybe generalized or inferred that a common ancestor entity in thesubsumption hierarchy, i.e. “root vegetables,” is also a solid. Thisallows reasoning to be performed on uncommon root vegetables that havelittle or no prior information about them in the ontology, e.g., a“skirret” is a root vegetable and thus, based on our generalizedknowledge of root vegetables, is most likely a solid.

FIG. 5 is a flowchart outlining an example operation for performing anautomated bootstrap learning process in accordance with one illustrativeembodiment. The operation outlined in FIG. 5 may be performed by aknowledge acquisition system, such as system 150 in FIG. 1, for example.While FIG. 5 outlines an operation for performance on a single attributethat is being learned, it should be appreciated that the same set ofoperations may be performed with regard to multiple attributes that areto be learned.

As shown in FIG. 5, the operation starts by defining an entity attributeto be learned, e.g., a state of matter attribute (step 502).Pre-condition features and post-condition features (the post-conditionfeatures are optional depending on the implementation) are defined for asmall set of action terms based on the particular entity attribute forwhich learning is to be performed, e.g., pre-condition featuresspecifying a state of matter required by the action term andpost-condition features specifying a state of matter that is generatedby the action term (step 504).

A next document, upon which the automated bootstrap based learningprocessing is to be performed, is received and natural languageprocessing is performed on the document to extract entity and actionterm features from the natural language content of the document (step506). For each identified entity in the document (steps 508-526 areperformed for each identified entity in the document), a temporallyordered action term listing is generated based on the results of thenatural language processing (step 508). The first action term in thecorresponding temporally ordered action term listing is identified andits pre-condition feature is transferred to an attribute value for theentity, e.g., if a first action term is “melt” and the pre-conditionfeature is “solid”, then the entity's state of matter attribute value isconsidered to include the value of “solid” as a candidate (step 510).

The post-condition feature of the action term is determined, e.g., thepost-condition feature of the action term “melt” may be “liquid”indicating that the action of melting generates a liquid (step 512). Thenext action term in the corresponding temporally ordered action termlisting, as well as its pre-condition feature, is identified (step 514).A determination is made as to whether the pre-condition feature of thenext action term matches the post-condition feature of the previousaction term (step 516). If there is a match, then the pre-conditionfeature of the next action term is transferred as a value to theattribute of the entity (step 518). It should be appreciated that thetransferring of values of pre-condition features to values forattributes of the entity includes the accumulation of counts of thenumber of times those specific values are encountered in the documentand across documents such that confidence scores may be later calculatedand used to determine which values of the attribute to maintain inassociation with the entity.

A determination is then made as to whether there are more action termsin the temporally ordered action term listing for the entity (step 520).If there are more action terms, then the operation returns to step 512where the operation of step 512 is performed on the action term whosepre-condition feature was compared to the post-condition feature of theprevious action term in the temporally ordered action term listing. Ifthere are no more action terms in the corresponding temporally orderedaction term listing for the entity, then the operation determineswhether there are more documents to be processed (step 522). If thereare more documents, the operation returns to step 506. Otherwise,confidence scores are calculated for each of the values of the attributeto be learned based on the accumulated evidence and the confidencescores are compared to a threshold confidence score value (step 524).For those attribute values that have confidence scores meeting orexceeding the threshold confidence score value, the attribute values aremaintained in association with the entity in an entity data structure ofa knowledge base or ontology (step 526). The operation then terminates.

It should be appreciated that while the above illustrative embodimentsare described with the action terms having a single pre-conditionfeature and single post-condition feature, the illustrative embodimentsare not limited to such. Rather, in other illustrative embodiments,multiple pre-condition features and post-condition features may beassociated with action terms, such as in the case where a separatepre-condition feature and post-condition feature may be defined for eachattribute of an entity upon which learning is being performed. As such,depending on the attribute(s) being learned, the correspondingpre-condition features and post-condition features may be evaluated inthe manner described above to transfer pre-condition feature values tothe attributes of the entity.

Thus, the result of this automated learning process outlined in FIG. 5is an expanded knowledge base that is expanded with regard to theattributes of the entities for the specific domain or in some casesadditional entities and relationships in the knowledge base. Theexpanded knowledge base may then be utilized to perform variouscognitive operations including performing question answering orresponding to requests for information, correction of natural languagedocuments or text, expanding upon the content of a natural languagedocument, training/testing of human users, automatic generation ofinstructions or commands for controlling the operation of an automatedsystem or device, performing monitoring of human actions or interactionswith entities and providing constructive feedback or instruction, and/orthe like. More details regarding specific embodiments in which variousones of these cognitive operations will be described in greater detailhereafter.

The cognitive operations performed based on the expanded knowledge baseor ontology generated by the mechanisms of the illustrative embodimentsmay be generally represented by the outline shown in FIG. 6. Theflowchart of FIG. 6 outlines a cognitive operation that may be performedby a cognitive system in accordance with one illustrative embodiment. Asshown in FIG. 6, the operation comprises receiving an input upon whichto perform a cognitive operation (step 610). This input may comprise aninput natural language question, an input request, an input documentthat is to be corrected or expanded upon, a set of instructions that areto be analyzed and added to in order to generate instructions/commandsfor controlling the operation of an automated system or device,automatically acquired audio/video input representing the actions of ahuman being or automated device with regard to entities in anenvironment, or the like.

The input is analyzed to extract information regarding entities andactions as well as any other suitable features for performance of theparticular cognitive operation (step 620). Based on the analysis of theinput by the cognitive system, a meaningful response or output isgenerated based on the expanded knowledge base or ontology 640 generatedvia the automated bootstrap based learning process 650 (step 630). Thegenerated result or output is then utilized to provide feedbackregarding the input (step 660). For example, this feedback may be ananswer to the input natural language question, a response to the inputrequest, a corrected document or suggestions for correcting thedocument, an output comprising an identification of errors in actionsbeing performed, or the like. The operation then terminates.

With regard to question answering or responding to requests, forexample, a user may submit a question/request directed to informationthat may be obtained from analysis of the knowledge base or ontology. Anexample question or request may be of the type “What ingredient can beused in place of butter in my recipe?” or “Show me what ingredient touse to replace butter in my recipe.” The knowledge base or ontology maybe searched to identify the alternatives for butter and the attributesof these alternatives, learned via the automated bootstrap basedlearning process of the illustrative embodiments, may be furtherevaluated to determine their appropriateness for inclusion in a user'srecipe, e.g., the state of matter attribute may indicate itsappropriateness for use as a replacement for butter depending on thestate of matter attribute of the butter in the recipe. Various types ofquestions or requests may be evaluated using the knowledge that isacquired by the automated learning process of the illustrativeembodiments.

With regard to correcting a document, such as a recipe, or filling ingaps in a series of instructions for performing an operation, e.g.,preparing a recipe, manufacturing an object, performing a laboratorytest, etc., a similar analysis as discussed above for expanding theknowledge base or ontology may be performed to identify mismatches inattributes of entities in a temporally ordered action term listing forthe entity. That is, similar to the above, during runtime analysis, acognitive system may analyze an input specifying a series of tasks to beperformed to generate a result, e.g., prepare a recipe, manufacture anobject, perform a lab test, etc., and thereby generate a temporallyordered action term listing from this input. The constraint terms of theaction terms may be compared to possible attribute values of the entityas well as confirmed against the previous actions being performed on theentity to verify that the entity has the correct attribute value as aresult of an action prior to the next action being performed. If amismatch is encountered, then a gap in the instructions may bedetermined or an incorrect instruction may be identified. A notificationof this error may be generated and output.

In some illustrative embodiments, a knowledge base of alternativeinstructions may be searched to identify one or more instructionsassociated with the entity which generates the correct attribute valuefor the entity. The one or more instructions may be used toautomatically modify the input to include a corresponding instruction,either in addition to or in replacement of the erroneous instruction.Alternatively, the one or more instructions may be output to a user forconsideration for inclusion or replacement of instructions in the input,as suggested changes or updates to the input. Using a recipe and cookingdomain as an example, if it is determined that the recipe calls forliquid butter, but there is no previous action for generating liquidbutter, then a knowledge base of instructions associated with butter maybe searched to find those instructions that result in liquid butter. Anexample of an instruction of “melt the butter” may be identified andoutput as a suggested change to the input recipe, automatically insertedinto the recipe, or the like. Such functionality may be provided as partof the operations 630 and 660 in FIG. 6.

Training/Testing System

The above illustrative embodiments illustrate mechanisms by which theillustrative embodiments perform bootstrapped automated learning ofattributes of entities in a knowledge base or ontology. As noted above,the illustrative embodiments provide mechanisms for performing cognitiveoperations, such as question answering, request processing, document orinstruction set correction/augmentation, or the like, based on theexpanded knowledge base or ontology generated via the automated learningprocess of the illustrative embodiments.

Further illustrative embodiments leverage this automatically acquiredknowledge to implement a training and/or testing system to assist withtraining or testing a human being, or in some cases automated systems ordevices, with regard to the performance of an operation that comprises aplurality of tasks, where the tasks may be dependent upon a state of anattribute of the entities upon which the task is performed, and wherethe state of the attribute of the entities may change based on tasksperformed on the entity. As an example, the training/testing system ofthe illustrative embodiments may be employed to train and/or test humanchefs with regard to preparation of a recipe and may provideconstructive feedback to the human being regarding the correctness orincorrectness of their responses or actions.

FIG. 7 is an example block diagram illustrating one illustrativeembodiment of a testing/training system employing the knowledgeacquisition system of the illustrative embodiments. It should beappreciated that, in FIG. 7, elements directed to systems may beimplemented in computing devices, data processing systems, or the like,such as server computing devices 104A-104D, client computing devices110, 112, or the like, that are specifically configured and executesoftware instructions for providing the functionality described.Moreover, in some illustrative embodiments, these computing devicesand/or data processing systems may have dedicated hardware that isspecifically configured to perform one or more of the functionsdescribed herein.

As shown in FIG. 7, a cognitive training/testing system 700 comprisescognitive system 710 which may employ a request processing pipeline 712.The cognitive training/testing system 700 may further comprise entitystate tracking data structures 720 for tracking the state of an entityaccording to the user responses, or detected actions, with regard to theentity. The state of the entity may be tracked and compared to theknowledge base 730 in response to verify the attributes of the entity atany particular state. These attributes may be used to verify userresponses to test/training outputs from the training/testing system 700.

The expanded knowledge base 730 generated based on the automatedbootstrap based learning process 760 of the knowledge acquisition system750 is provided as input to the training/testing system 700 along withtraining/testing resources 740. The expanded knowledge base 730,training/testing resources 740, and state tracking data structures 720may be considered the “corpus” or corpora upon which the processingpipeline 712 of the cognitive system 710 may operate, for example. Thetraining/testing resources 740 may comprise various bases for sendingtraining/testing output to a user via the user's system 770, such astest question sets, training materials, or the like, that specify anoperation comprising a series of tasks to be performed with regard toone or more entities to accomplish the operation or generate a desiredresult. In one illustrative embodiment, the training/testing resources740 may include recipes for testing the skills of the user, training theuser to prepare the recipes, or otherwise evaluate the actions detectedby the user on entities with regard to the preparation of a recipe. Insome illustrative embodiments, the training/testing resources 740 mayfurther comprise the manually tagged seed action terms set 745 thatspecifies the pre-condition features and post-condition features of theaction terms.

The training/testing system 700 may output training/testing output to auser system 770 based on the training/testing resources 740 totrain/test the human user of the user system 770. For example, atraining/test question may be output to the user via a graphical userinterface 780 output on the user system 770. The user may enter aresponse to the training/test question via the interface 780 which maythen be evaluated by the training/testing system 700 based on thecurrent state of entities as indicated in the state tracker datastructures 720, processing of the response and training/test question bythe cognitive system 710 and processing pipeline 712, and the knowledgerepresented in the expanded knowledge base 730. Corresponding outputsmay be generated by the training/testing system 700 and output to theuser via the interface 780 to inform the user of thecorrectness/incorrectness of the response, reasoning for anyincorrectness, and possible correct responses as well as reasoning forthe correctness of these possible correct response. The evaluation ofthe correctness/incorrectness, as well as the generation of the correctresponse and reasoning may all be informed by the expanded knowledgebase 730.

As an example, the training/testing system 700 of these illustrativeembodiments may be used, again using the cooking domain as an example,to train a chef to prepare a recipe where the chef is questioned as tothe steps of the recipe and the correctness of those answers isdetermined by the cognitive system 710 by evaluating the question/answerpair via the pipeline 712, based on the current attributes of theingredients as tracked in the corresponding state tracking datastructures 720. Assume that a user of user system 770 wishes to learnhow to prepare the recipe shown in FIG. 4 and sends a request to thetraining/testing system 700 to instruct the user how to prepare amacaroni and cheese dish with chard as an ingredient. Thetraining/testing system 700 may retrieve a corresponding recipe from atraining/testing resources 740 data structure which specifies theingredients and actions to be performed to prepare the recipe, e.g., anordered listing of instructions.

The training/testing system 700 may, via the output of the interface780, send a listing of ingredients for the recipe to the user andrequest that the user indicate the first step in preparing the mixture.The user may indicate that the butter should be poured over the onions.The analysis of the recipe by the training/testing system 700 indicatesthat that butter is currently in a solid state, using state trackingdata structures 720. Based on the knowledge base 730 it is determinedthat butter can be in both a solid state and a liquid state. Moreover,form the action terms 745, it is determined that the action of pouringin the user's response requires an entity in a liquid state, and anaction of melting may transform a solid into a liquid. Thus, based onthe current state of the butter, it is determined by thetraining/testing system 700 that the user's response is incorrect withthe reason being an incorrect state of the ingredient, i.e. the butteris solid and the action of pouring has a pre-condition feature of theingredient being a liquid. Moreover, the training/testing system 700further determines that the correct response is to convert theingredient from a solid state to a liquid state and that the action of“melting” may be performed to achieve that objective. Furthermore thetraining/testing system 700 may verify that the particular ingredient,e.g., butter in this example, may exist in both solid and liquid formand thus, the objective is achievable with this ingredient. Thus, thetraining/testing system 700 may output a response to the user via theinterface 780 indicating the incorrectness of the user response, thereason, a correct response, and the reasoning of for the correctresponse.

FIG. 8A is an example diagram of a training/testing system prompt outputthat may be provided to a user via a user client system in accordancewith one illustrative embodiment. As shown in FIG. 8A, thetraining/testing system outputs a prompt to the user via the user system770 and graphical user interface 780, an example of which is depicted inFIG. 8A. In this example, the training/testing system prompt includes alisting of ingredients 810 and a question 820 of the type “What is thefirst step for generating the macaroni and cheese mixture?” The questionis followed by an answer field 830 through which the user may input theuse's natural language response to the training/testing question. Inthis case, the user may input the answer “Pour the butter over theonions.” The user's response to the training/testing question 820 istransmitted to the training/testing system which uses the cognitivesystem 710 and processing pipeline 712 to perform natural languageprocessing of the user's answer and evaluate the answer to the questionbased on the current state of the ingredients from the state trackingdata structures 720, the training/testing resources 740, and theknowledge in the expanded knowledge base 730.

FIG. 8B is an example diagram of an example response of atraining/testing system to a user input in accordance with oneillustrative embodiment. As shown in FIG. 8B, the response 840 comprisesan indicator that the user's response was incorrect 850, the reason theresponse was incorrect 860, a correct answer 870, and a reason for thecorrect answer 880. In this example, the reason 860 was incorrect isindicated to be that the ingredient (butter) is solid and pouring thebutter requires the butter to be a liquid. The correct answer 870 wouldbe to “melt the butter” and the reason 880 why this is a correctresponse is because melting the butter generates liquid butter which canthen be poured.

It should be appreciated that this is only one example of atraining/testing system which may be implemented using the expandedknowledge base generated using an automated bootstrap learning processin accordance with the illustrative embodiments. Other types oftraining/testing systems may also be implemented and used to train/testhuman users with regard to an operation comprising a plurality of tasksoperating on entities and in which the attributes of the entities affectthe tasks or dictate what tasks may be performed on, to, or with theentities.

It should be appreciated that while a graphical user input basedinteraction between the user and the training/testing system 700 may beused with one illustrative embodiment for evaluating the responses of auser to training/testing prompts, other training/testing systems 700 mayutilize other mechanisms for obtaining user input that may be evaluatedby the training/testing system 700 for correctness/incorrectness andproviding appropriate instruction, confirmation, or correction. Forexample, in some illustrative embodiments, the user system 770 mayfurther include various multi-media input devices, such as videocameras, audio input devices (e.g., microphones), and the like, throughwhich the actual actions on entities and audible responses may bemonitored to generate user input to the training/testing system 700.

That is, the input devices (not shown) of the user system 770 maycapture video input of the user while the user is performing tasks theybelieve will achieve a desired objective for which they are beingtrained/tested, e.g., preparation of a recipe in the cooking domainexample. The video/audio input may be provided to the training/testingsystem 700 which may perform various processing of the input, such asimage recognition analysis, speech-to-text conversion, and the like, toconvert the input to a form that the training/testing system 700 mayoperate on to verify the actions being performed on an entity. Forexample, image recognition may be used to identify both the entity theuser is operating on and the action that the user is performing. Thisinformation may be evaluated in a manner previously described above,such as by determining the state of the entity, determining constraintsof the action being performed, comparing to knowledge base information,comparing to training/testing resources, and the like, to determine thecorrectness/incorrectness of the action being performed on the entity,the reason for the incorrectness, a correct action to be performed on anentity, and the reason for the correctness of the correct action. Thisinformation may then be output to the user via the user system 770. Forexample, an audible message may be output indicating the informationdetermined from the evaluation.

FIG. 9 is a flowchart outlining an example operation for providing atraining/testing functionality in accordance with one illustrativeembodiment. The operation outlined in FIG. 9 may be performed by acombination of the knowledge acquisition system and training/testingsystem of FIG. 7, for example.

As shown in FIG. 9, the operation starts by generating an expandedknowledge base for a specific domain for which training/testing of auser is to be performed (step 910). The expanded knowledge based isgenerated using an automated bootstrap based learning process of aknowledge acquisition system in the manner previously described above.The domain specific expanded knowledge base is input to atraining/testing system along with domain specific training/testingresources comprising information (step 920). The training/testing systemgenerates a training/testing prompt output that prompts the user for aresponse (step 930). A user response to the prompt is received (step940) and the response is processed by the training/testing system toextract features of the response, e.g., identification of entities,identification of action terms, etc. (step 950).

The features of the user response are evaluated against the domainspecific expanded knowledge base, the training/testing resources, and acurrent state of the entities as indicated by an entity state trackingdata structure (step 960). A determination is made as to whether theuser response is correct or incorrect and if incorrect, the reason whythe response is incorrect based on the current state of the entities,the attributes of the entity as indicated by the domain specificexpanded knowledge base, and the training/testing resources (step 970).If incorrect, a correct response may be identified and a reason for thecorrect response may be generated (step 980) and a corresponding outputmay be provided to the user to inform them of thecorrectness/incorrectness of the user's response, the reason for anyincorrectness, a correct response, and a reason for the correct response(step 990). The operation then terminates.

Automatic Correction/Insertion of Ordered Set of Tasks

In still further illustrative embodiments, mechanisms are provided forperforming automatic insertion or correction of an ordered set of tasksfor completing an operation or achieving an objective in a particulardomain. The insertion/correction may be performed, in some illustrativeembodiments, to augment or otherwise complete an already existing set oftasks or instructions for performing tasks. For example, again using thecooking domain as an example, a recipe may be provided that has missingor incorrect instructions. The mechanisms of the illustrativeembodiments may be implemented to verify the entity/action paircorrectness and thereby determine if there are missing instructions inthe recipe and what the nature of those missing instructions may be withregard to correction entity/action pairs. As a simple example, considera recipe that calls for the pouring of butter into a mixture. The recipemay not previously have had an instruction to melt the butter prior tothe pouring instruction and thus, a missing instruction may bedetermined to exist.

The automatic correction/insertion of ordered sets of tasks based on thedetection of missing tasks or gaps in a set of tasks may be used withautomated systems that are used to perform the operation or achieve theobjective. For example, computing systems, robotic systems, and thelike, may make use of the mechanism of the illustrative embodiments tocorrect/insert instructions for such automated systems when missinginstructions or gaps in instructions are determined to exist. Forexample, in one illustrative embodiment, a robotic chef system may beimplemented in which a robotic system is employed to prepare a recipe.The recipe may have missing instructions or gaps in instructions due tohuman error, for example, where the human being generating the recipeassumes knowledge that the robotic system or computing system does nothave. The mechanisms of the illustrative embodiments may implement theautomated bootstrap based learning process to expand a domain specificknowledge base and that information may be utilized to “fill in theblanks” of the missing instructions or gaps in the instructions. Theidentification of the missing instructions or gaps in instructions mayimplement a similar process of evaluating entity states with regard toaction term constraints and post-condition features to identifymismatches in the a temporally ordered action term list for each entity.

FIG. 10 is an example block diagram illustrating one illustrativeembodiment of an automated instruction execution system employing theknowledge acquisition system of the illustrative embodiments. As shownin FIG. 10, the system comprises an automatic instruction system 1000comprising an operation instructions modification engine 1010 whichmodifies an input set of instructions 1020 for performing an operationbased on an evaluation of the set of instructions 1020 using the domainspecific expanded knowledge base 1030 generated by a knowledgeacquisition system 1050 implementing an automated bootstrap basedlearning process 1060 in accordance with one or more of the illustrativeembodiments described above. The resulting modified set of instructionscomprising instructions that fill the missing instructions or gaps ininstructions in the original set 1020, may be provided to an automatedsystem 1040, such as a robotic system, to perform the set ofinstructions including those that fill in the missing instructions orgaps.

For example, assume that the automated system 1040 is a robotic chefsystem that is configured to perform actions on ingredients so as toprepare a recipe represented by a set of instructions from the automatedsystem operations 1020. The automatic instruction system 1000 mayreceive a request for the automated system 1040 to prepare a specificrecipe, for example, and the corresponding recipe may be retrieved fromthe automated system operations storage 1020. The automatic instructionsystem 1000 may convert the recipe to a set of instructions that may beexecuted by the automated system 1040, if the recipe is not already in aform that may be executed by the automated system 1040. The automaticinstruction system 1000 may also provide the recipe to the knowledgeacquisition system 1050 which may evaluate the recipe in a similarmanner as discussed above when performing expansion of the knowledgebase 1030.

That is, the knowledge acquisition system 1050 may parse the recipe andgenerate a temporally ordered action list for each entity in the recipe.The temporal state of the corresponding entity may be tracked bytraversing the action terms in the temporally ordered action list, e.g.,the butter is first melted and thus, the state of the butter went fromsolid to liquid, the butter was then poured into a mixture and thus, thestate of the butter is a mixture state (which may indicate that actionsto the butter by itself can no longer be performed and thus, subsequentactions that do not reference the mixture and instead reference thebutter may be erroneous). The temporally ordered action list may beprocessed to determine if there are any mismatches between pre-conditionfeatures of action terms and the then temporal state of the entity.Similar to the mechanisms shown in FIG. 7, in some illustrativeembodiments, entity state tracking data structures 1015 may be used totrack the temporal state of the entities.

If there are mismatches between pre-condition features of action termsand current temporal state of the entity, then it may be determined thatthere is a missing instruction or set of instructions in the recipe. Forexample, assume that a first action term indicates that the butter hasbeen divided meaning that the butter was initially in a solid state andis rendered into a divided solid state. The next action term in thetemporally ordered action term listing may indicate that the butter isto be poured into a mixture. The pre-condition feature of the actionterm “pour” requires a liquid entity, e.g., a liquid form of aningredient, however by tracking the temporal state of the entity, it isdetermined that the current state of the butter entity is a dividedsolid. As a result, a mismatch is identified between the pre-conditionfeature of the action term and the current state of the entity. Theerror may be noted and a corresponding notification generated and sentto the automatic instruction system 1000 to inform the automaticinstruction system 1000 of the mismatch and potential missinginstructions or gaps.

Thereafter, the set of instructions may be automatically corrected orupdated by the operation instruction modification engine 1010 to includeany missing instructions required to provide the entity with therequired attribute value based on the identification of mismatches inthe constraints and the attributes of the entities. In some illustrativeembodiments, the detection of this mismatch or error indicates missinginstructions and an instruction knowledge base 1070 may be searched bythe operation instruction modification engine 1010 for instructions thatresult in the required attribute for the entity required by thepre-condition feature of the action term, e.g., an instruction thatresults in liquid butter for use with a subsequent action term thatrequires liquid butter as part of its pre-condition feature, e.g., meltthe butter. Additional cognitive operations may be performed by theoperation instruction modification engine 1010 to ensure compatibilityof the discovered instructions with the other instructions present inthe existing listing of instructions, e.g., in a recipe, set ofmanufacturing instructions, or the like.

Similarly, at each stage of checking the pre-condition features of anaction and the current state of the entity, it may also be determined,based on the domain specific expanded knowledge base 1030, whether anaction term requires a certain attribute of the entity and whether ornot the entity can or cannot have that value of the attribute, e.g.,none of the values for the attribute exist in association with theentity, then an error may be identified. For example, if it isdetermined that an action term has a pre-condition feature that theentity must be in liquid form, e.g., the action term “pour”, but none ofthe state of matter attribute values for the entity allow for a liquidstate of matter, then an error is determined for which no missinginstructions can be provided. In such a case, the error may simply beused to generate a notification of an uncorrectable error to a systemadministrator or the like (not shown).

The resulting modified set of instructions, comprising the instructionsfor filling in the missing instructions or gaps in instructions, ma beprovided to the automated system 1040, which may be a robotic systemsuch as a robotic chef in this example. The automated system 1040 maythen implement the modified set of instructions to perform the operationor achieve the desired result, e.g., prepare the recipe.

Thus, the automatic instruction system 1000 may be provided with aninitial sparsely populated set of instructions to achieve a desiredobjective, e.g., generate a food item according to a recipe, generate anobject according to a set of manufacturing instructions, perform aspecific test on an entity based on a set of instructions, or the like.Based on this sparsely populated set of instructions, a dynamicdetermination of the missing intervening instructions, corresponding tothe instruction gaps, may be automatically performed and correspondinginstructions for filling the gaps may be automatically generated orselected based on analysis of the attributes of entities andrecognizable action terms in the sparsely populated set of instructions.The mechanisms of the illustrative embodiments thus, “fill in theblanks” and provide the necessary additional instructions to instructthe automated system to perform the missing operations to achieve thedesired result.

FIG. 11 is a flowchart outlining an example operation for performingautomated correction/insertion of instructions for an automated systemin accordance with one illustrative embodiment. The operation outlinedin FIG. 11 may be implemented by a combination of the knowledgeacquisition system and automatic instruction system of FIG. 10, forexample.

As shown in FIG. 11, the operation starts by generating an expandedknowledge base for a specific domain for which an operation is to beperformed by an automated system (step 1110). The expanded knowledgebase is generated using an automated bootstrap based learning process ofa knowledge acquisition system in the manner previously described above.The knowledge base may be provided to an automated instruction systemfor use in generating/selecting instructions for filling in identifiedmissing instructions or gaps in instructions of an initial set ofinstructions.

The automatic instruction system receives a request to perform anoperation using an automated system (step 1120). A corresponding initialset of instructions is retrieved/generated for performing the operation(step 1130). The initial set of instructions is parsed and processed bya knowledge acquisition system to identify any discrepancies betweenconstrain requirements of actions to be performed and the temporal stateof an entity (step 1140).

Any discrepancies are notified to the automatic instruction system (step1150) which then performs further processing (including a search of aninstruction knowledge base) or otherwise generates instructions forachieving a desired temporal state of the entity upon which the actionwhose constraint requirement is not satisfied by the initial set ofinstructions (step 1160). The initial set of instructions is thenmodified to include the identified/generated instructions (step 1170)and the modified set of instructions is output to the automated systemfor automated execution of the modified set of instructions (step 1180).The operation then terminates.

Situations might arise when multiple actions have the rightpost-condition feature to correct a given set of instructions. In suchsituations, the system may rely on additional evidence coming from thecorpus domain (e.g., prior recipes). Such evidence may take the form ofthe frequency of co-occurrence of the action and ingredient. Anillustrative example comes from the domain of cooking. For example,assume a recipe calls for “sprinkling the bread over the chicken”. Thesystem recognizes that bread is “solid” while “sprinkle” requires anargument of “particulate”. A search of the knowledge base returns a setof actions that transform a “solid” into a “particulate,” such as“pulverize”, “grind”, “crumble”, and the like. To select the mostappropriate action from this list, the system may perform additionalprocessing, such as gathering co-occurrence evidence between the action(“pulverize”, “grind”, and “crumble”) and the object (“bread”). Becausethe most frequent action performed on bread from among these is crumble,this action may be selected. Such co-occurrence information may comedirectly from text, but it may still be affected by the problemsidentified earlier (e.g. prior actions performed on an ingredient changeits state). Therefore, a knowledge resource like the one built using themechanisms described earlier may be leveraged.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a communication bus, such as a system bus,for example. The memory elements can include local memory employedduring actual execution of the program code, bulk storage, and cachememories which provide temporary storage of at least some program codein order to reduce the number of times code must be retrieved from bulkstorage during execution. The memory may be of various types including,but not limited to, ROM, PROM, EPROM, EEPROM, DRAM, SRAM, Flash memory,solid state memory, and the like.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening wired or wireless I/O interfaces and/orcontrollers, or the like. I/O devices may take many different formsother than conventional keyboards, displays, pointing devices, and thelike, such as for example communication devices coupled through wired orwireless connections including, but not limited to, smart phones, tabletcomputers, touch screen devices, voice recognition devices, and thelike. Any known or later developed I/O device is intended to be withinthe scope of the illustrative embodiments.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modems and Ethernet cards are just a few of thecurrently available types of network adapters for wired communications.Wireless communication based network adapters may also be utilizedincluding, but not limited to, 802.11 a/b/g/n wireless communicationadapters, Bluetooth wireless adapters, and the like. Any known or laterdeveloped network adapters are intended to be within the spirit andscope of the present invention.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the describedembodiments. The embodiment was chosen and described in order to bestexplain the principles of the invention, the practical application, andto enable others of ordinary skill in the art to understand theinvention for various embodiments with various modifications as aresuited to the particular use contemplated. The terminology used hereinwas chosen to best explain the principles of the embodiments, thepractical application or technical improvement over technologies foundin the marketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method, in a data processing system comprisinga processor and a memory accessible by the processor, for training ahuman user to perform an operation, the method comprising: generating,by the data processing system, a domain specific knowledge basecomprising a set of entities and corresponding domain specificattributes; expanding, by the data processing system, the domainspecific knowledge base to include values for the domain specificattributes through an automated bootstrap learning process that performsnatural language processing and analysis of natural language contentusing a set of pre-condition annotated action terms, thereby generatingan expanded domain specific knowledge base; obtaining, by the dataprocessing system, an input from another device identifying an actionassociated with an entity in the set of entities; retrieving, by thedata processing system, from the expanded domain specific knowledgebase, a domain specific attribute value for the entity identified in theinput and a pre-condition annotation associated with the actionidentified in the input; evaluating, by the data processing system, acorrectness or incorrectness of the input based on the retrieved domainspecific attribute value and the retrieved pre-condition annotation; andoutputting, by the data processing system, a notification to a usercomputing device indicating whether the input is correct or incorrect tothereby train a user associated with the user computing device.
 2. Themethod of claim 1, wherein the bootstrap learning process associates avalue with a domain specific attribute of an entity, in the set ofentities, based on a pre-condition value of a pre-condition annotationof an instance of an action term, in the set of pre-condition annotatedaction terms, that is correlated with the entity in the natural languagecontent.
 3. The method of claim 2, wherein the another device is theuser computing device, and wherein the input is an input from the userin response to a training inquiry presented to the user based on theexpanded domain specific knowledge base.
 4. The method of claim 1,wherein the another device is a sensor device that detects an input froma user representative of the action and the entity.
 5. The method ofclaim 4, wherein the sensor device is at least one of an image capturingdevice or an audio capturing device, and wherein the evaluation of thecorrectness or incorrectness of the input comprises performing at leastone of image analysis or audio analysis to identify the action and theentity.
 6. The method of claim 1, wherein evaluating, by the dataprocessing system, a correctness or incorrectness of the input based onthe retrieved domain specific attribute value and the retrievedpre-condition annotation further comprises: evaluating a post-conditionof a previous action performed with regard to the entity to determine anew value for the domain specific attribute indicating a result of theperformance of the previous action on the entity; and correlating thenew value for the domain specific attribute with the retrievedpre-condition annotation.
 7. The method of claim 1, whereinpre-condition annotations of the set of pre-condition annotated actionterms specify, for each pre-condition annotated action term, a requiredvalue of a domain specific attribute of an entity upon which an actioncorresponding to the pre-condition annotated action term may becorrectly performed.
 8. The method of claim 1, further comprisingmaintaining, by the data processing system, a temporal ordering ofactions performed with regard to the entity identified in the input,wherein evaluating the correctness or incorrectness of the input basedon the retrieved domain specific attribute value and the retrievedpre-condition annotation is further performed based on a current stateof the entity as determined from a post-condition annotation associatedwith an action term corresponding to a last performed action in thetemporal ordering of actions performed with regard to the entity.
 9. Themethod of claim 1, wherein the entities in the set of entities areingredients for cooking recipes, and wherein the action terms in the setof pre-condition annotated action terms are actions that are able to beperformed on the ingredients, and wherein, for each pre-conditionannotation action term in the set of pre-condition action terms, apre-condition annotation of the pre-condition annotated action termspecifies a state of matter of an ingredient required in order for acorresponding action to be correctly performed on the ingredient. 10.The method of claim 1, wherein the entities in the set of entities andthe pre-condition annotated action terms in the set of pre-conditionannotated action terms are associated with a domain in which temporallyordered tasks are to be followed to complete an operation.
 11. Acomputer program product comprising a computer readable storage mediumhaving a computer readable program stored therein, wherein the computerreadable program, when executed on a data processing system, causes thedata processing system to: generate a domain specific knowledge basecomprising a set of entities and corresponding domain specificattributes; expand the domain specific knowledge base to include valuesfor the domain specific attributes through an automated bootstraplearning process that performs natural language processing and analysisof natural language content using a set of pre-condition annotatedaction terms, thereby generating an expanded domain specific knowledgebase; obtain an input from another device identifying an actionassociated with an entity in the set of entities; retrieve, from theexpanded domain specific knowledge base, a domain specific attributevalue for the entity identified in the input and a pre-conditionannotation associated with the action identified in the input; evaluatea correctness or incorrectness of the input based on the retrieveddomain specific attribute value and the retrieved pre-conditionannotation; and output a notification to a user computing deviceindicating whether the input is correct or incorrect to thereby train auser associated with the user computing device.
 12. The computer programproduct of claim 11, wherein the bootstrap learning process associates avalue with a domain specific attribute of an entity, in the set ofentities, based on a pre-condition value of a pre-condition annotationof an instance of an action term, in the set of pre-condition annotatedaction terms, that is correlated with the entity in the natural languagecontent.
 13. The computer program product of claim 12, wherein theanother device is the user computing device, and wherein the input is aninput from the user in response to a training inquiry presented to theuser based on the expanded domain specific knowledge base.
 14. Thecomputer program product of claim 11, wherein the another device is asensor device that detects an input from a user representative of theaction and the entity.
 15. The computer program product of claim 14,wherein the sensor device is at least one of an image capturing deviceor an audio capturing device, and wherein the evaluation of thecorrectness or incorrectness of the input comprises performing at leastone of image analysis or audio analysis to identify the action and theentity.
 16. The computer program product of claim 11, wherein thecomputer readable program further causes the data processing system toevaluate a correctness or incorrectness of the input based on theretrieved domain specific attribute value and the retrievedpre-condition annotation further at least by: evaluating apost-condition of a previous action performed with regard to the entityto determine a new value for the domain specific attribute indicating aresult of the performance of the previous action on the entity; andcorrelating the new value for the domain specific attribute with theretrieved pre-condition annotation.
 17. The computer program product ofclaim 11, wherein pre-condition annotations of the set of pre-conditionannotated action terms specify, for each pre-condition annotated actionterm, a required value of a domain specific attribute of an entity uponwhich an action corresponding to the pre-condition annotated action termmay be correctly performed.
 18. The computer program product of claim11, wherein the computer readable program further causes the dataprocessing system to maintain a temporal ordering of actions performedwith regard to the entity identified in the input, wherein evaluatingthe correctness or incorrectness of the input based on the retrieveddomain specific attribute value and the retrieved pre-conditionannotation is further performed based on a current state of the entityas determined from a post-condition annotation associated with an actionterm corresponding to a last performed action in the temporal orderingof actions performed with regard to the entity.
 19. The computer programproduct of claim 11, wherein the entities in the set of entities areingredients for cooking recipes, and wherein the action terms in the setof pre-condition annotated action terms are actions that are able to beperformed on the ingredients, and wherein, for each pre-conditionannotation action term in the set of pre-condition action terms, apre-condition annotation of the pre-condition annotated action termspecifies a state of matter of an ingredient required in order for acorresponding action to be correctly performed on the ingredient.
 20. Anapparatus comprising: a processor; and a memory coupled to theprocessor, wherein the memory comprises instructions which, whenexecuted by the processor, cause the processor to: generate a domainspecific knowledge base comprising a set of entities and correspondingdomain specific attributes; expand the domain specific knowledge base toinclude values for the domain specific attributes through an automatedbootstrap learning process that performs natural language processing andanalysis of natural language content using a set of pre-conditionannotated action terms, thereby generating an expanded domain specificknowledge base; obtain an input from another device identifying anaction associated with an entity in the set of entities; retrieve, fromthe expanded domain specific knowledge base, a domain specific attributevalue for the entity identified in the input and a pre-conditionannotation associated with the action identified in the input; evaluatea correctness or incorrectness of the input based on the retrieveddomain specific attribute value and the retrieved pre-conditionannotation; and output a notification to a user computing deviceindicating whether the input is correct or incorrect to thereby train auser associated with the user computing device.